From 5656d155d6c3ea15b6708ba0d5ef891eb315c123 Mon Sep 17 00:00:00 2001 From: kennypavan Date: Mon, 12 Oct 2026 17:40:03 -0700 Subject: [PATCH 1/2] Increased permutations for all datasets --- .gitignore | 1 + manuscript/alzheimers_coexpression_null.py | 18 +- .../alzheimers_coexpression_null_merge.py | 93 + manuscript/feature_comparision.py | 276 +- manuscript/feature_comparison_hpc.py | 896 ++ .../robustness/Supplemental_Table_1.xlsx | Bin 43304 -> 43673 bytes ...lzheimers_coexpression_null_flip_rates.csv | 4771 ++++-- ...lzheimers_coexpression_null_flip_rates.pdf | Bin 37266 -> 54117 bytes ...lzheimers_coexpression_null_flip_rates.png | Bin 433258 -> 390800 bytes ...imers_coexpression_null_gene_frequency.csv | 84 +- .../alzheimers_coexpression_null_panels.csv | 3075 ++++ .../alzheimers_coexpression_null_pvalues.csv | 4 +- .../alzheimers_coexpression_null_runs.csv | 127 +- ...lzheimers_coexpression_null_flip_rates.csv | 1016 ++ .../alzheimers_coexpression_null_panels.csv | 1067 ++ 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manuscript/robustness/microglia/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv create mode 100644 manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_flip_rates.csv create mode 100644 manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_panels.csv create mode 100644 manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv create mode 100644 manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_flip_rates.csv create mode 100644 manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_panels.csv create mode 100644 manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_runs.csv diff --git a/.gitignore b/.gitignore index 217a64f..0846bc3 100644 --- a/.gitignore +++ b/.gitignore @@ -23,3 +23,4 @@ examples/data/ # run logs logs/ *.log +*.sh \ No newline at end of file diff --git a/manuscript/alzheimers_coexpression_null.py b/manuscript/alzheimers_coexpression_null.py index 979ecf5..a21e77a 100644 --- a/manuscript/alzheimers_coexpression_null.py +++ b/manuscript/alzheimers_coexpression_null.py @@ -6,6 +6,12 @@ Usage: python alzheimers_coexpression_null.py [--out-dir DIR] [--n-shuffles N] [--n-parallel N] [--cell-type TYPE] [--n-cells N] [--balance-groups] + [--first-shuffle K --batch-dir DIR] + +To add shuffles to an existing run, pass --first-shuffle (one past the last +saved shuffle) and --batch-dir. The batch's runs, flip_rates and panels are +written there and the saved outputs are left alone; combine them with +alzheimers_coexpression_null_merge.py. Outputs (to DIR): - alzheimers_coexpression_null_runs.csv: Real and shuffled run summaries @@ -80,6 +86,10 @@ def main() -> None: parser.add_argument("--n-shuffles", type=int, default=25) parser.add_argument("--n-parallel", type=int, default=None, help="Shuffles run at once (default: about one per 4 cores)") + parser.add_argument("--first-shuffle", type=int, default=1, + help="Index of the first shuffle (seeds continue from here)") + parser.add_argument("--batch-dir", type=Path, default=None, + help="Write only this batch's raw tables here, for merging") add_loading_args(parser) args = parser.parse_args() out_dir = args.out_dir @@ -95,7 +105,13 @@ def main() -> None: print(f"Running co-expression null ({args.n_shuffles} shuffles)...") res = model.coexpression_null(n_shuffles=args.n_shuffles, n_parallel=args.n_parallel, - verbose=10) + verbose=10, first_shuffle=args.first_shuffle) + if args.batch_dir is not None: + args.batch_dir.mkdir(parents=True, exist_ok=True) + for key in ("runs", "flip_rates", "panels"): + res[key].to_csv(args.batch_dir / f"alzheimers_coexpression_null_{key}.csv", index=False) + print(f"Batch written to {args.batch_dir}") + return for key in ("runs", "flip_rates", "panels", "gene_frequency", "pvalues"): res[key].to_csv(out_dir / f"alzheimers_coexpression_null_{key}.csv", index=False) plot_flip_rates(res["flip_rates"], model.flip_rate_percentage, diff --git a/manuscript/alzheimers_coexpression_null_merge.py b/manuscript/alzheimers_coexpression_null_merge.py new file mode 100644 index 0000000..ba02dd4 --- /dev/null +++ b/manuscript/alzheimers_coexpression_null_merge.py @@ -0,0 +1,93 @@ +"""Add batches of extra shuffles to a saved co-expression null. + +Reads the saved outputs of alzheimers_coexpression_null.py in DIR and every +batch under DIR/null_batches/ (written with --first-shuffle and --batch-dir), +checks that each batch refit the same observed panel, then rewrites the +outputs from all shuffles together. Per-gene p-values get a Benjamini-Hochberg +adjusted column (p_adj) across the observed panel. + +Usage: + python alzheimers_coexpression_null_merge.py --out-dir DIR + +Outputs (to DIR, overwritten): +- alzheimers_coexpression_null_runs.csv +- alzheimers_coexpression_null_flip_rates.csv +- alzheimers_coexpression_null_panels.csv +- alzheimers_coexpression_null_gene_frequency.csv (with p_adj) +- alzheimers_coexpression_null_pvalues.csv +- alzheimers_coexpression_null_flip_rates.png / .pdf +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import matplotlib.pyplot as plt +import pandas as pd +from scipy.stats import false_discovery_control + +import recursieve +from alzheimers_coexpression_null import plot_flip_rates + +PREFIX = "alzheimers_coexpression_null_" +TABLES = ("runs", "flip_rates", "panels") + + +def read_tables(folder: Path) -> dict: + return {key: pd.read_csv(folder / f"{PREFIX}{key}.csv") for key in TABLES} + + +def observed_panel(panels: pd.DataFrame) -> list: + obs = panels[panels["run"] == "observed"].sort_values("rank") + return obs["gene"].tolist() + + +def main() -> None: + plt.rcParams.update({"font.family": "Arial", "font.size": 12, + "pdf.fonttype": 42, "ps.fonttype": 42}) + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--out-dir", type=Path, required=True) + parser.add_argument("--flip-threshold", type=float, default=0.01, + help="Stop threshold drawn on the flip-rate plot") + args = parser.parse_args() + out_dir = args.out_dir + + saved = read_tables(out_dir) + panel = observed_panel(saved["panels"]) + batches = sorted((out_dir / "null_batches").glob("*/")) + if not batches: + raise SystemExit(f"No batches in {out_dir / 'null_batches'}") + + merged = {key: [saved[key]] for key in TABLES} + for batch in batches: + tables = read_tables(batch) + if observed_panel(tables["panels"]) != panel: + raise SystemExit(f"{batch.name}: observed panel differs from the saved one") + for key in TABLES: + df = tables[key] + merged[key].append(df[df["run"] != "observed"]) + print(f"Added {batch.name}") + + # rerunning the merge must not count a shuffle twice + runs = pd.concat(merged["runs"], ignore_index=True).drop_duplicates("run", keep="first") + flips = pd.concat(merged["flip_rates"], ignore_index=True).drop_duplicates(["run", "iteration"]) + panels = pd.concat(merged["panels"], ignore_index=True).drop_duplicates(["run", "rank"]) + + freq = recursieve.recursieve._null_gene_frequency(panels) + freq["p_adj"] = false_discovery_control(freq["p_value"], method="bh") + pvalues = recursieve.recursieve._null_pvalues(runs) + + outputs = {"runs": runs, "flip_rates": flips, "panels": panels, + "gene_frequency": freq, "pvalues": pvalues} + for key, df in outputs.items(): + df.to_csv(out_dir / f"{PREFIX}{key}.csv", index=False) + plot_flip_rates(flips, args.flip_threshold, out_dir / f"{PREFIX}flip_rates") + + n = runs["run"].ne("observed").sum() + print(f"{n} shuffles; {int((freq['p_adj'] < 0.05).sum())}/{len(freq)} genes with p_adj < 0.05") + print(pvalues.round(4).to_string(index=False)) + + +if __name__ == "__main__": + main() diff --git a/manuscript/feature_comparision.py b/manuscript/feature_comparision.py index 0ad8a4f..39c21c5 100644 --- a/manuscript/feature_comparision.py +++ b/manuscript/feature_comparision.py @@ -10,20 +10,36 @@ skew group_1 right-tailed, group_2 left-tailed, same mean coexpression same marginals, staged genes correlated only in group_2 -Trial i uses sim_seed + i. +Each dataset is fixed (sim_seed, stage_seed) for every trial; only +recursieve's seed changes, so trial i uses recursieve seed rs_seed + i. +Wilcoxon, TTest and LogReg are deterministic, so they run once per dataset. +With --datasets N, dataset d (from --first-dataset) uses sim_seed + d and +stage_seed + d, and the heatmap SEM is taken across per-dataset means. + +Heatmap cells show the mean with the SEM across trials beneath it. + +DE panels are filtered twice: on raw p-values (n_recovered) and on BH-adjusted +p-values (n_recovered_adj). recursieve does not filter on p-values, so its two +columns are the same. Both are saved; only the adjusted counts are plotted, with +a note on the correction at the bottom of each figure. Outputs (to --out-dir): feature_comparison_wide_counts.csv - feature_comparison_wide_heatmap.png / .pdf + feature_comparison_wide_heatmap_adj.png / .pdf Usage: python feature_comparision.py --seeds 5 python feature_comparision.py --scenarios bimodal --seeds 3 + python feature_comparision.py --seeds 25 --datasets 25 --tag _multi + +With more than one dataset, a violin plot of every run per method is also +written: feature_comparison_wide_violins_adj.png / .pdf """ import warnings warnings.filterwarnings("ignore") import argparse +import textwrap import time from pathlib import Path @@ -31,7 +47,7 @@ import pandas as pd import anndata as ad import scanpy as sc -from scipy.stats import skewnorm +from scipy.stats import skewnorm, ttest_ind from scsim import scsim import recursieve @@ -47,7 +63,9 @@ def _background(sim_seed, ncells, ngenes): libloc=7.64, libscale=0.78, mean_rate=7.68, mean_shape=0.34, expoutprob=0.00286, expoutloc=6.15, expoutscale=0.49, - diffexpprob=0.025, diffexpdownprob=0., diffexploc=1.0, diffexpscale=1.0, + # almost no background group DE: staged genes keep the totals of the genes they + # replace, so a DE gene there would leak a mean difference into the block + diffexpprob=0.001, diffexpdownprob=0., diffexploc=1.0, diffexpscale=1.0, bcv_dispersion=0.448, bcv_dof=22.087, ndoublets=0, nproggenes=400, progdownprob=0., progdeloc=1.0, progdescale=1.0, progcellfrac=0.35, proggoups=None, @@ -81,6 +99,24 @@ def _staged_totals(adata, n_staged): return np.asarray(adata.X)[:, :n_staged].sum(axis=1).astype(np.int64) +def check_staging(adata, staged, p_min=1e-4): + """Stop if the staged block's per-cell totals differ between the groups. + + Staged genes keep each cell's total of the genes they replace, so a group + difference in those totals would give every staged gene a real mean + difference. Welch t-test on log1p totals; clean backgrounds give p > 0.009. + """ + if not staged: + return + totals = np.asarray(adata.X)[:, :len(staged)].sum(axis=1) + g2 = (adata.obs["group"] == "group_2").to_numpy() + p = ttest_ind(np.log1p(totals[g2]), np.log1p(totals[~g2]), equal_var=False).pvalue + if p < p_min: + ratio = totals[g2].mean() / totals[~g2].mean() + raise SystemExit(f"staged block differs between groups (group_2/group_1 total " + f"{ratio:.2f}, p = {p:.2g}); staged genes would carry a mean difference") + + def build_null(sim_seed=32, stage_seed=0, n_staged=10, ncells=2000, ngenes=2000, **_): """Labels shuffled, no staged genes.""" @@ -200,32 +236,24 @@ def _normalized(adata): def _filter_ranked_genes(adata, method, n_top=50, pval_cutoff=0.05): - """Top ranked genes with p < pval_cutoff (logreg has no p-values).""" + """Top ranked genes with p < pval_cutoff, as (raw p panel, adjusted p panel). + + logreg has no p-values, so both panels are its top n_top genes. + """ a = _normalized(adata) sc.tl.rank_genes_groups(a, groupby="group", groups=["group_2"], reference="group_1", method=method, use_raw=False) rankings = a.uns["rank_genes_groups"] names = np.asarray(rankings["names"]["group_2"]) - pvals = None + panels = [] for key in ("pvals", "pvals_adj"): if key not in rankings: + panels.append(names[:n_top].tolist()) continue - candidate = rankings[key] - if isinstance(candidate, dict): - pvals = candidate.get("group_2", None) - else: - if getattr(candidate, "dtype", None) is not None and hasattr(candidate.dtype, "names"): - pvals = candidate["group_2"] if "group_2" in candidate.dtype.names else None - else: - pvals = candidate - if pvals is not None: - break - if pvals is not None: - pvals = np.asarray(pvals, dtype=float) + pvals = np.asarray(rankings[key]["group_2"], dtype=float) mask = np.isfinite(pvals) & (pvals < pval_cutoff) - filtered = names[mask].tolist() - return filtered[:n_top] - return names[:n_top].tolist() + panels.append(names[mask].tolist()[:n_top]) + return tuple(panels) def run_wilcoxon(adata, n_top=50, pval_cutoff=0.05): @@ -264,6 +292,7 @@ def count_recovered(panel, staged): TITLE = "Genes Recovered" SUBTITLE = "(Simulated)" +SUBTITLE_ADJ = "(Simulated, adjusted p)" METHOD_LABELS = {"recursieve": "RecurSieve"} def _normalize_scenario_labels(df): @@ -291,8 +320,19 @@ def _mixed_text(fig, parts, x, y, transform, vertical=False, **kw): start += n -def plot_heatmap(df, out_dir, n_staged, scenarios): - """Mean genes recovered per scenario and method.""" +def correction_note(pval_cutoff): + """Figure note on how the DE panels were filtered.""" + return (f"Wilcoxon and t-test panels: genes with Benjamini\u2013Hochberg adjusted " + f"p < {pval_cutoff:g} (scanpy rank_genes_groups). LogReg has no p-values " + "(top genes by score). RecurSieve does not filter on p-values.") + + +def plot_heatmap(df, out_dir, n_staged, scenarios, value="n_recovered", + subtitle=SUBTITLE, suffix="", tag="", note=None): + """Mean genes recovered per scenario and method. + + SEM is across seeds for one dataset, or across per-dataset means for several. + """ import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt @@ -309,14 +349,27 @@ def plot_heatmap(df, out_dir, n_staged, scenarios): ordered_scenarios = [s for s in SCENARIO_ORDER if s in scenarios] missing = [s for s in scenarios if s not in ordered_scenarios] ordered_scenarios.extend(missing) + n_ds = df["dataset"].nunique() + n_seeds = int(df.groupby(["scenario", "method", "dataset"]).size().max()) + if n_ds > 1: + sem_note = (f"Values are mean \u00b1 SEM across {n_ds} dataset means " + f"({n_seeds} RecurSieve seeds each).") + df = df.groupby(["scenario", "method", "dataset"], as_index=False)[value].mean() + else: + sem_note = (f"Values are mean \u00b1 SEM across {n_seeds} RecurSieve seeds on one " + "dataset; DE methods are deterministic, so their SEM is 0.") + note = f"{sem_note} {note}" if note else sem_note piv = (df.pivot_table(index="scenario", columns="method", - values="n_recovered")[cols].reindex(ordered_scenarios, fill_value=0.0)) + values=value)[cols].reindex(ordered_scenarios, fill_value=0.0)) M = np.nan_to_num(piv.to_numpy(dtype=float), nan=0.0) + sem = (df.pivot_table(index="scenario", columns="method", values=value, + aggfunc="sem")[cols].reindex(ordered_scenarios)) + S = np.nan_to_num(sem.to_numpy(dtype=float), nan=0.0) cmap = LinearSegmentedColormap.from_list("seq_blue", _RAMP) fig, ax = plt.subplots(figsize=(max(7.5, 1.3 * M.shape[1] + 2.4), 0.85 * M.shape[0] + 1.9)) - fig.subplots_adjust(left=0.22, right=0.86, top=0.88, bottom=0.2) + fig.subplots_adjust(left=0.22, right=0.86, top=0.88, bottom=0.29) fig.patch.set_facecolor(_SURFACE); ax.set_facecolor(_SURFACE) mesh = ax.pcolormesh(M, cmap=cmap, vmin=0, vmax=n_staged, edgecolors=_SURFACE, linewidth=2) @@ -325,9 +378,11 @@ def plot_heatmap(df, out_dir, n_staged, scenarios): v = M[i, j] if np.isfinite(v): label = "0.0" if abs(v) < 1e-12 else f"{v:.1f}" - ax.text(j + 0.5, i + 0.5, label, ha="center", va="center", - fontsize=8, - color=_SURFACE if v > n_staged * 0.55 else _INK) + color = _SURFACE if v > n_staged * 0.55 else _INK + ax.text(j + 0.5, i + 0.42, label, ha="center", va="center", + fontsize=8, color=color) + ax.text(j + 0.5, i + 0.68, f"\u00b1{S[i, j]:.2f}", ha="center", + va="center", fontsize=6, color=color) ax.set_xticks(np.arange(M.shape[1]) + 0.5) ax.set_xticklabels([METHOD_LABELS.get(c, c) for c in cols], rotation=35, @@ -346,10 +401,80 @@ def plot_heatmap(df, out_dir, n_staged, scenarios): cbar.outline.set_visible(False) cbar.ax.tick_params(length=0, labelsize=15, colors=_INK) - _mixed_text(fig, [(TITLE + " ", {"fontweight": "bold"}), (SUBTITLE, {"fontstyle": "italic"})], + _mixed_text(fig, [(TITLE + " ", {"fontweight": "bold"}), (subtitle, {"fontstyle": "italic"})], 0.5, 1.05, ax.transAxes, fontsize=14, color=_INK) + fig.text(0.5, 0.02, textwrap.fill(note, 125), ha="center", va="bottom", fontsize=8, + color=_MUTED) + for ext in ("png", "pdf"): + fig.savefig(out_dir / f"feature_comparison_wide{tag}_heatmap{suffix}.{ext}", + dpi=300, facecolor=_SURFACE) + plt.close(fig) + + +def plot_outcome_violins(df, out_dir, n_staged, scenarios, value="n_recovered", + subtitle=SUBTITLE, suffix="", tag="", note=None): + """One panel per scenario: a violin per method over every run (all datasets + and seeds), with each run as a gray dot. A method whose runs are all equal + is drawn as a flat line. + """ + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + + plt.rcParams.update({"font.family": "Arial", "pdf.fonttype": 42, "ps.fonttype": 42}) + df = _normalize_scenario_labels(df) + cols = [m for m in METHOD_ORDER if m in set(df["method"])] + n_ds = df["dataset"].nunique() + n_runs = int(df.groupby(["scenario", "method"]).size().max()) + fill, edge = _RAMP[3], _RAMP[6] + + fig, axes = plt.subplots(1, len(scenarios), figsize=(2.3 * len(scenarios) + 0.8, 3.4), + sharey=True) + axes = np.atleast_1d(axes) + fig.patch.set_facecolor(_SURFACE) + for ax, scen in zip(axes, scenarios): + ax.set_facecolor(_SURFACE) + for j, m in enumerate(cols): + v = df[(df["scenario"] == scen) & (df["method"] == m)][value].to_numpy(dtype=float) + # one dot per run, spread sideways; counts stay at their true value + jitter = np.random.default_rng(j).uniform(-0.2, 0.2, len(v)) + ax.scatter(j + jitter, v, s=5, color=_MUTED, alpha=0.4, linewidths=0, zorder=4) + if np.ptp(v) == 0: + # no spread to draw a density from + ax.plot([j - 0.3, j + 0.3], [v[0]] * 2, color=edge, lw=2, zorder=3) + continue + parts = ax.violinplot(v, positions=[j], widths=0.75, showextrema=False) + for body in parts["bodies"]: + body.set_facecolor(fill) + body.set_edgecolor(edge) + body.set_linewidth(1) + body.set_alpha(1) + body.set_zorder(3) + ax.set_title(scen, fontsize=12, color=_INK) + ax.set_xticks(range(len(cols))) + ax.set_xticklabels([METHOD_LABELS.get(c, c) for c in cols], rotation=40, + ha="right", fontsize=10, color=_INK) + ax.set_xlim(-0.6, len(cols) - 0.4) + ax.set_ylim(-0.35, n_staged + 0.35) + ax.set_yticks(range(0, n_staged + 1, 2)) + ax.grid(axis="y", color="#e4e3df", lw=0.8) + ax.set_axisbelow(True) + for side in ("top", "right", "bottom"): + ax.spines[side].set_visible(False) + ax.spines["left"].set_color(_MUTED) + ax.tick_params(colors=_MUTED, length=0) + axes[0].set_ylabel(f"genes recovered (of {n_staged})", fontsize=11, color=_INK) + fig.suptitle(f"Genes Recovered {subtitle}", fontsize=13, color=_INK, + fontweight="bold", x=0.5, y=0.97, va="top") + fig.text(0.5, 0.875, f"{n_runs} runs per method ({n_ds} datasets \u00d7 " + f"{n_runs // n_ds} seeds); gray dots: single runs; flat line: every run " + "recovered the same number", + ha="center", va="top", fontsize=9, color=_MUTED) + fig.tight_layout(rect=(0, 0.07 if note else 0, 1, 0.86)) + if note: + fig.text(0.5, 0.02, note, ha="center", va="bottom", fontsize=9, color=_MUTED) for ext in ("png", "pdf"): - fig.savefig(out_dir / f"feature_comparison_wide_heatmap.{ext}", + fig.savefig(out_dir / f"feature_comparison_wide{tag}_violins{suffix}.{ext}", dpi=300, facecolor=_SURFACE) plt.close(fig) @@ -373,7 +498,11 @@ def main(): ap.add_argument("--ncells", type=int, default=2000) ap.add_argument("--ngenes", type=int, default=2000) ap.add_argument("--sim-seed", type=int, default=32, - help="base seed; trial i uses sim_seed+i") + help="dataset seed, fixed across trials") + ap.add_argument("--stage-seed", type=int, default=0, + help="staged-gene seed, fixed across trials") + ap.add_argument("--rs-seed", type=int, default=42, + help="base recursieve seed; trial i uses rs_seed+i") ap.add_argument("--max-iterations", type=int, default=50) ap.add_argument("--center", type=float, default=8.0) ap.add_argument("--sd", type=float, default=1.5) @@ -384,6 +513,11 @@ def main(): ap.add_argument("--out-dir", default=str(Path(__file__).resolve().parent / "robustness" / "feature_comparison")) ap.add_argument("--plot-only", action="store_true", help="plot the saved CSV without rerunning") + ap.add_argument("--datasets", type=int, default=1, + help="datasets to run; dataset d uses sim_seed+d and stage_seed+d") + ap.add_argument("--first-dataset", type=int, default=0) + ap.add_argument("--tag", default="", + help="suffix for the CSV and heatmap names, e.g. _multi") args = ap.parse_args() scenarios = [x.strip() for x in args.scenarios.split(",")] @@ -393,46 +527,76 @@ def main(): out_dir = Path(args.out_dir) out_dir.mkdir(parents=True, exist_ok=True) - csv = out_dir / "feature_comparison_wide_counts.csv" - columns = ["scenario", "seed", "method", "n_recovered", "panel_size"] + csv = out_dir / f"feature_comparison_wide{args.tag}_counts.csv" + columns = ["dataset", "scenario", "seed", "method", "n_recovered", "panel_size", + "n_recovered_adj", "panel_size_adj"] df = pd.DataFrame(columns=columns) if csv.exists(): df = _normalize_scenario_labels(pd.read_csv(csv, keep_default_na=False, na_values=[])) + if "dataset" not in df.columns: # a single-dataset CSV has no dataset column + df.insert(0, "dataset", 0) + keys = ["dataset", "scenario", "seed", "method"] if not args.plot_only: rows = [] - for scen in scenarios: + for ds, scen in [(d, s) for d in range(args.first_dataset, args.first_dataset + args.datasets) + for s in scenarios]: + # one dataset per scenario; DE methods are deterministic on it + adata, staged = BUILDERS[scen]( + sim_seed=args.sim_seed + ds, stage_seed=args.stage_seed + ds, + n_staged=args.n_staged, ncells=args.ncells, ngenes=args.ngenes, + center=args.center, sd=args.sd, sep=args.sep, alpha=args.alpha) + check_staging(adata, staged) + # each panel is (raw p panel, adjusted p panel) + de_panels = { + "Wilcoxon": run_wilcoxon(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), + "TTest": run_ttest(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), + "LogReg": run_logreg(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), + } for seed in range(args.seeds): t0 = time.time() - adata, staged = BUILDERS[scen]( - sim_seed=args.sim_seed + seed, stage_seed=seed, - n_staged=args.n_staged, ncells=args.ncells, ngenes=args.ngenes, - center=args.center, sd=args.sd, sep=args.sep, alpha=args.alpha) - panels = { - "Wilcoxon": run_wilcoxon(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), - "TTest": run_ttest(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), - "LogReg": run_logreg(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), - "recursieve": run_recursieve(adata, n_top=args.n_top, - max_iterations=args.max_iterations, - pval_cutoff=args.pval_cutoff), - } + panels = dict(de_panels) + rs = run_recursieve( + adata, n_top=args.n_top, seed=args.rs_seed + seed, + max_iterations=args.max_iterations, pval_cutoff=args.pval_cutoff) + rs_n, rs_size = count_recovered(rs, staged), len(rs) line = [] for m in METHOD_ORDER: - n = count_recovered(panels[m], staged) - rows.append(dict(scenario=scen, seed=seed, method=m, - n_recovered=n, panel_size=len(panels[m]))) - line.append(f"{m}={n}") - print(f" [{scen} s{seed}] " + " ".join(line) + f" [{time.time()-t0:.0f}s]", flush=True) - df = pd.concat([df, pd.DataFrame(rows)], ignore_index=True) - df = df.drop_duplicates(subset=["scenario", "seed", "method"], keep="last").reset_index(drop=True) - df.to_csv(csv, index=False) + if m == "recursieve": + # recursieve does not filter on p, so raw and adjusted match + n, size, n_adj, size_adj = rs_n, rs_size, rs_n, rs_size + else: + raw, adj = panels[m] + n, size = count_recovered(raw, staged), len(raw) + n_adj, size_adj = count_recovered(adj, staged), len(adj) + rows.append(dict(dataset=ds, scenario=scen, seed=seed, method=m, + n_recovered=n, panel_size=size, + n_recovered_adj=n_adj, panel_size_adj=size_adj)) + line.append(f"{m}={n}/{n_adj}") + print(f" [d{ds} {scen} s{seed}] " + " ".join(line) + f" [{time.time()-t0:.0f}s]", flush=True) + # save after each dataset and scenario so a crash loses little + out = pd.concat([df, pd.DataFrame(rows)], ignore_index=True) + out["dataset"] = out["dataset"].astype(int) + out.drop_duplicates(subset=keys, keep="last").to_csv(csv, index=False) + df = pd.read_csv(csv, keep_default_na=False, na_values=[]) + df = _normalize_scenario_labels(df) if df.empty: raise SystemExit(f"No benchmark data in {csv}") order = ordered_scenarios(df, scenarios) - piv = df.pivot_table(index="scenario", columns="method", values="n_recovered") + n_ds = df["dataset"].nunique() + extra = f", {n_ds} datasets" if n_ds > 1 else "" + value, suffix = "n_recovered_adj", "_adj" + subtitle = SUBTITLE_ADJ.replace(")", extra + ")") + note = correction_note(args.pval_cutoff) + piv = df.pivot_table(index="scenario", columns="method", values=value) + print(value) print(piv[[m for m in METHOD_ORDER if m in piv.columns]].reindex(order).round(2).to_string()) - plot_heatmap(df, out_dir, args.n_staged, order) + plot_heatmap(df, out_dir, args.n_staged, order, value=value, subtitle=subtitle, + suffix=suffix, tag=args.tag, note=note) + if n_ds > 1: + plot_outcome_violins(df, out_dir, args.n_staged, order, value=value, + subtitle=subtitle, suffix=suffix, tag=args.tag, note=note) if __name__ == "__main__": main() diff --git a/manuscript/feature_comparison_hpc.py b/manuscript/feature_comparison_hpc.py new file mode 100644 index 0000000..f67a2d5 --- /dev/null +++ b/manuscript/feature_comparison_hpc.py @@ -0,0 +1,896 @@ +"""Multi-dataset staged gene recovery benchmark (standalone, for HPC). + +Simulates N datasets per scenario, runs Wilcoxon, t-test and logistic +regression (scanpy) once per dataset and RecurSieve with S seeds per dataset, +and counts how many of the 10 staged genes each method's top 50 panel recovers. +DE panels keep genes with Benjamini-Hochberg adjusted p < 0.05. + +Dataset d uses sim_seed + d and stage_seed + d; RecurSieve seed i uses +rs_seed + i. + +Scenarios: + null labels shuffled, no staged genes + bimodal group_2 has two modes around the group_1 mean + trimodal group_2 has three modes around the group_1 mean + skew group_1 right-tailed, group_2 left-tailed, same mean + coexpression same marginals, staged genes correlated only in group_2 + +The CSV is saved after every dataset and scenario, and a rerun skips the +blocks that are already complete, so an interrupted job can be resubmitted. + +Chunk mode (to use many nodes): --make-todo splits the unfinished work into +chunks of --seed-chunk seeds (default 5) and writes them to todo_blocks.csv. +Array task i of N (--block-index i --n-tasks N) runs chunks i, i+N, ... and +writes each to parts/block_d__s-.csv +(run_feature_blocks.sh). --plot-only merges these with everything else. + +Every simulated dataset is checked before use: if the staged genes' per-cell +totals differ between groups (Welch t-test p < 1e-4), the run stops. + +Outputs (to --out-dir): + feature_comparison_wide_multi_counts.csv + feature_comparison_wide_multi_heatmap_adj.png / .pdf + (mean of the dataset means; SEM across dataset means) + +Requires: recursieve, scanpy, anndata, numpy, pandas, scipy, matplotlib. + +Usage: + python feature_comparison_hpc.py --datasets 25 --seeds 25 --out-dir results + python feature_comparison_hpc.py --out-dir results --plot-only + python feature_comparison_hpc.py --out-dir results --make-todo + python feature_comparison_hpc.py --out-dir results --n-tasks 100 --block-index 3 +""" +import warnings +warnings.filterwarnings("ignore") + +import argparse +import sys +import textwrap +import time +from pathlib import Path + +import numpy as np +import pandas as pd +import anndata as ad +import scanpy as sc +from scipy.stats import skewnorm, ttest_ind + +import recursieve + + +# --------------------------------------------------------------------------- +# scsim single-cell count simulator (Kotliar et al., github.com/dylkot/scsim), +# copied unchanged from manuscript/scsim.py so this script is self-contained. +# --------------------------------------------------------------------------- +class scsim: + def __init__(self, ngenes=10000, ncells=100, seed=757578, + mean_rate=.3, mean_shape=.6, libloc=11, libscale=0.2, + expoutprob=.05, expoutloc=4, expoutscale=0.5, ngroups=1, + diffexpprob=.1, diffexpdownprob=.5, + diffexploc=.1, diffexpscale=.4, bcv_dispersion=.1, + bcv_dof=60, ndoublets=0, groupprob=None, + nproggenes=None, progdownprob=None, progdeloc=None, + progdescale=None, proggoups=None, progcellfrac=None, + minprogusage=.2, maxprogusage=.8): + + self.ngenes = ngenes + self.ncells = ncells + self.seed = seed + self.mean_rate = mean_rate + self.mean_shape = mean_shape + self.libloc = libloc + self.libscale = libscale + self.expoutprob = expoutprob + self.expoutloc = expoutloc + self.expoutscale = expoutscale + self.ngroups = ngroups + self.diffexpprob = diffexpprob + self.diffexpdownprob = diffexpdownprob + self.diffexploc = diffexploc + self.diffexpscale = diffexpscale + self.bcv_dispersion = bcv_dispersion + self.bcv_dof = bcv_dof + self.ndoublets = ndoublets + self.init_ncells = ncells+ndoublets + self.nproggenes=nproggenes + self.progdownprob=progdownprob + self.progdeloc=progdeloc + self.progdescale=progdescale + self.proggoups=proggoups + self.progcellfrac = progcellfrac + self.minprogusage = minprogusage + self.maxprogusage = maxprogusage + + if groupprob is None: + self.groupprob = [1/float(self.ngroups)]*self.ngroups + elif (len(groupprob) == self.ngroups) & (np.abs(np.sum(groupprob) - 1) < (10**-6)): + self.groupprob = groupprob + else: + sys.exit('Invalid groupprob input') + + + def simulate(self): + np.random.seed(self.seed) + print('Simulating cells') + self.cellparams = self.get_cell_params() + print('Simulating gene params') + self.geneparams = self.get_gene_params() + + if (self.nproggenes is not None) and (self.nproggenes > 0): + print('Simulating program') + self.simulate_program() + + print('Simulating DE') + self.sim_group_DE() + + print('Simulating cell-gene means') + self.cellgenemean = self.get_cell_gene_means() + if self.ndoublets > 0: + print('Simulating doublets') + self.simulate_doublets() + + print('Adjusting means') + self.adjust_means_bcv() + print('Simulating counts') + self.simulate_counts() + + def simulate_counts(self): + '''Sample read counts for each gene x cell from Poisson distribution + using the variance-trend adjusted updatedmean value''' + self.counts = pd.DataFrame(np.random.poisson(lam=self.updatedmean), + index=self.cellnames, columns=self.genenames) + + def adjust_means_bcv(self): + '''Adjust cellgenemean to follow a mean-variance trend relationship''' + self.bcv = self.bcv_dispersion + (1 / np.sqrt(self.cellgenemean)) + chisamp = np.random.chisquare(self.bcv_dof, size=self.ngenes) + self.bcv = self.bcv*np.sqrt(self.bcv_dof / chisamp) + self.updatedmean = np.random.gamma(shape=1/(self.bcv**2), + scale=self.cellgenemean*(self.bcv**2)) + self.bcv = pd.DataFrame(self.bcv, index=self.cellnames, columns=self.genenames) + self.updatedmean = pd.DataFrame(self.updatedmean, index=self.cellnames, + columns=self.genenames) + + + def simulate_doublets(self): + ## Select doublet cells and determine the second cell to merge with + d_ind = sorted(np.random.choice(self.ncells, self.ndoublets, + replace=False)) + d_ind = ['Cell%d' % (x+1) for x in d_ind] + self.cellparams['is_doublet'] = False + self.cellparams.loc[d_ind, 'is_doublet'] = True + extraind = self.cellparams.index[-self.ndoublets:] + group2 = self.cellparams.ix[extraind, 'group'].values + self.cellparams['group2'] = -1 + self.cellparams.loc[d_ind, 'group2'] = group2 + + ## update the cell-gene means for the doublets while preserving the + ## same library size + dmean = self.cellgenemean.loc[d_ind,:].values + dmultiplier = .5 / dmean.sum(axis=1) + dmean = np.multiply(dmean, dmultiplier[:, np.newaxis]) + omean = self.cellgenemean.loc[extraind,:].values + omultiplier = .5 / omean.sum(axis=1) + omean = np.multiply(omean, omultiplier[:,np.newaxis]) + newmean = dmean + omean + libsize = self.cellparams.loc[d_ind, 'libsize'].values + newmean = np.multiply(newmean, libsize[:,np.newaxis]) + self.cellgenemean.loc[d_ind,:] = newmean + ## remove extra doublet cells from the data structures + self.cellgenemean.drop(extraind, axis=0, inplace=True) + self.cellparams.drop(extraind, axis=0, inplace=True) + self.cellnames = self.cellnames[0:self.ncells] + + + def get_cell_gene_means(self): + '''Calculate each gene's mean expression for each cell while adjusting + for the library size''' + + + group_genemean = self.geneparams.loc[:,[x for x in self.geneparams.columns if ('_genemean' in x) and ('group' in x)]].T.astype(float) + group_genemean = group_genemean.div(group_genemean.sum(axis=1), axis=0) + ind = self.cellparams['group'].apply(lambda x: 'group%d_genemean' % x) + + if self.nproggenes == 0: + cellgenemean = group_genemean.loc[ind,:].astype(float) + cellgenemean.index = self.cellparams.index + else: + noprogcells = self.cellparams['has_program']==False + hasprogcells = self.cellparams['has_program']==True + + print(' - Getting mean for activity program carrying cells') + progcellmean = group_genemean.loc[ind[hasprogcells], :] + progcellmean.index = ind.index[hasprogcells] + progcellmean = progcellmean.multiply(1-self.cellparams.loc[hasprogcells, 'program_usage'], axis=0) + + progmean = self.geneparams.loc[:,['prog_genemean']] + progmean = progmean.div(progmean.sum(axis=0), axis=1) + progusage = self.cellparams.loc[progcellmean.index, ['program_usage']] + progusage.columns = ['prog_genemean'] + progcellmean += progusage.dot(progmean.T) + progcellmean = progcellmean.astype(float) + + print(' - Getting mean for non activity program carrying cells') + noprogcellmean = group_genemean.loc[ind[noprogcells],:] + noprogcellmean.index = ind.index[noprogcells] + + cellgenemean = pd.concat([noprogcellmean, progcellmean], axis=0) + + del(progcellmean, noprogcellmean) + + cellgenemean = cellgenemean.reindex(index=self.cellparams.index) + + print(' - Normalizing by cell libsize') + normfac = (self.cellparams['libsize'] / cellgenemean.sum(axis=1)).values + for col in cellgenemean.columns: + cellgenemean[col] = cellgenemean[col].values*normfac + #cellgenemean = cellgenemean.multiply(normfac, axis=0).astype(float) + return(cellgenemean) + + + def get_gene_params(self): + '''Sample each genes mean expression from a gamma distribution as + well as identifying outlier genes with expression drawn from a + log-normal distribution''' + basegenemean = np.random.gamma(shape=self.mean_shape, + scale=1./self.mean_rate, + size=self.ngenes) + + is_outlier = np.random.choice([True, False], size=self.ngenes, + p=[self.expoutprob,1-self.expoutprob]) + outlier_ratio = np.ones(shape=self.ngenes) + outliers = np.random.lognormal(mean=self.expoutloc, + sigma=self.expoutscale, + size=is_outlier.sum()) + outlier_ratio[is_outlier] = outliers + gene_mean = basegenemean.copy() + median = np.median(basegenemean) + gene_mean[is_outlier] = outliers*median + self.genenames = ['Gene%d' % i for i in range(1, self.ngenes+1)] + geneparams = pd.DataFrame([basegenemean, is_outlier, outlier_ratio, gene_mean], + index=['BaseGeneMean', 'is_outlier', 'outlier_ratio', 'gene_mean'], + columns=self.genenames).T + return(geneparams) + + + def get_cell_params(self): + '''Sample cell group identities and library sizes''' + groupid = self.simulate_groups() + libsize = np.random.lognormal(mean=self.libloc, sigma=self.libscale, + size=self.init_ncells) + self.cellnames = ['Cell%d' % i for i in range(1, self.init_ncells+1)] + cellparams = pd.DataFrame([groupid, libsize], + index=['group', 'libsize'], + columns=self.cellnames).T + cellparams['group'] = cellparams['group'].astype(int) + return(cellparams) + + + def simulate_program(self): + ## Simulate the program gene expression + self.geneparams['prog_gene'] = False + proggenes = self.geneparams.index[-self.nproggenes:] + self.geneparams.loc[proggenes, 'prog_gene'] = True + DEratio = np.random.lognormal(mean=self.progdeloc, + sigma=self.progdescale, + size=self.nproggenes) + DEratio[DEratio<1] = 1 / DEratio[DEratio<1] + is_downregulated = np.random.choice([True, False], + size=len(DEratio), + p=[self.progdownprob, + 1-self.progdownprob]) + DEratio[is_downregulated] = 1. / DEratio[is_downregulated] + all_DE_ratio = np.ones(self.ngenes) + all_DE_ratio[-self.nproggenes:] = DEratio + prog_mean = self.geneparams['gene_mean']*all_DE_ratio + self.geneparams['prog_genemean'] = prog_mean + + ## Assign the program to cells + self.cellparams['has_program'] = False + if self.proggoups is None: + ## The program is active in all cell types + self.proggoups = np.arange(1, self.ngroups+1) + + self.cellparams.loc[:, 'program_usage'] = 0 + for g in self.proggoups: + groupcells = self.cellparams.index[self.cellparams['group']==g] + hasprog = np.random.choice([True, False], size=len(groupcells), + p=[self.progcellfrac, + 1-self.progcellfrac]) + self.cellparams.loc[groupcells[hasprog], 'has_program'] = True + usages = np.random.uniform(low=self.minprogusage, + high=self.maxprogusage, + size=len(groupcells[hasprog])) + self.cellparams.loc[groupcells[hasprog], 'program_usage'] = usages + + + + + def simulate_groups(self): + '''Sample cell group identities from a categorical distriubtion''' + groupid = np.random.choice(np.arange(1, self.ngroups+1), + size=self.init_ncells, p=self.groupprob) + self.groups = np.unique(groupid) + return(groupid) + + + def sim_group_DE(self): + '''Sample differentially expressed genes and the DE factor for each + cell-type group''' + groups = self.cellparams['group'].unique() + if self.nproggenes>0: + proggene = self.geneparams['prog_gene'].values + else: + proggene = np.array([False]*self.geneparams.shape[0]) + + for group in self.groups: + isDE = np.random.choice([True, False], size=self.ngenes, + p=[self.diffexpprob,1-self.diffexpprob]) + isDE[proggene] = False # Program genes shouldn't be differentially expressed between groups + DEratio = np.random.lognormal(mean=self.diffexploc, + sigma=self.diffexpscale, + size=isDE.sum()) + DEratio[DEratio<1] = 1 / DEratio[DEratio<1] + is_downregulated = np.random.choice([True, False], + size=len(DEratio), + p=[self.diffexpdownprob,1-self.diffexpdownprob]) + DEratio[is_downregulated] = 1. / DEratio[is_downregulated] + all_DE_ratio = np.ones(self.ngenes) + all_DE_ratio[isDE] = DEratio + group_mean = self.geneparams['gene_mean']*all_DE_ratio + + deratiocol = 'group%d_DEratio' % group + groupmeancol = 'group%d_genemean' % group + self.geneparams[deratiocol] = all_DE_ratio + self.geneparams[groupmeancol] = group_mean + + +# --------------------------------------------------------------------------- +# benchmark (same code as manuscript/feature_comparision.py) +# --------------------------------------------------------------------------- +METHOD_ORDER = ["Wilcoxon", "TTest", "LogReg", "recursieve"] +SCENARIO_ORDER = ["null", "bimodal", "trimodal", "skew", "coexpression"] + + +def _background(sim_seed, ncells, ngenes): + """Simulated background counts.""" + sim = scsim( + ngenes=ngenes, ncells=ncells, ngroups=2, + libloc=7.64, libscale=0.78, + mean_rate=7.68, mean_shape=0.34, + expoutprob=0.00286, expoutloc=6.15, expoutscale=0.49, + # almost no background group DE: staged genes keep the totals of the genes they + # replace, so a DE gene there would leak a mean difference into the block + diffexpprob=0.001, diffexpdownprob=0., diffexploc=1.0, diffexpscale=1.0, + bcv_dispersion=0.448, bcv_dof=22.087, ndoublets=0, + nproggenes=400, progdownprob=0., progdeloc=1.0, progdescale=1.0, + progcellfrac=0.35, proggoups=None, + minprogusage=0.1, maxprogusage=0.7, seed=sim_seed, + ) + sim.simulate() + a = ad.AnnData(X=sim.counts, obs=sim.cellparams, var=sim.geneparams) + a.var_names = a.var_names.astype(str) + a.obs["group"] = a.obs["group"].apply( + lambda x: "group_1" if x == 1 else "group_2") + return a + + +def _match_block_totals(pattern, totals, rng=None): + """Resample a staged pattern so each cell keeps its staged block total.""" + pattern = np.maximum(np.asarray(pattern, dtype=float), 0.0) + s = pattern.sum(axis=1, keepdims=True) + s[s == 0] = 1.0 + prob = pattern / s + totals = np.asarray(totals, dtype=np.int64) + if rng is None: + rng = np.random.default_rng(0) + out = np.empty(pattern.shape, dtype=np.int64) + for i in range(pattern.shape[0]): + out[i] = rng.multinomial(totals[i], prob[i]) + return out + + +def _staged_totals(adata, n_staged): + """Per-cell sum of the staged genes.""" + return np.asarray(adata.X)[:, :n_staged].sum(axis=1).astype(np.int64) + + +def check_staging(adata, staged, p_min=1e-4): + """Stop if the staged block's per-cell totals differ between the groups. + + Staged genes keep each cell's total of the genes they replace, so a group + difference in those totals would give every staged gene a real mean + difference. Welch t-test on log1p totals; clean backgrounds give p > 0.009. + """ + if not staged: + return + totals = np.asarray(adata.X)[:, :len(staged)].sum(axis=1) + g2 = (adata.obs["group"] == "group_2").to_numpy() + p = ttest_ind(np.log1p(totals[g2]), np.log1p(totals[~g2]), equal_var=False).pvalue + if p < p_min: + ratio = totals[g2].mean() / totals[~g2].mean() + raise SystemExit(f"staged block differs between groups (group_2/group_1 total " + f"{ratio:.2f}, p = {p:.2g}); staged genes would carry a mean difference") + + +def build_null(sim_seed=32, stage_seed=0, n_staged=10, ncells=2000, + ngenes=2000, **_): + """Labels shuffled, no staged genes.""" + adata = _background(sim_seed, ncells, ngenes) + rng = np.random.default_rng(stage_seed) + adata.obs["group"] = pd.Categorical( + rng.permutation(adata.obs["group"].values)) + return adata, [] + + +def build_bimodal(sim_seed=32, stage_seed=0, n_staged=10, ncells=2000, + ngenes=2000, center=8.0, sd=1.5, sep=4.0, **_): + """group_1 normal, group_2 bimodal about the same center.""" + adata = _background(sim_seed, ncells, ngenes) + rng = np.random.default_rng(stage_seed) + m1 = (adata.obs["group"] == "group_1").values + n1, n2 = int(m1.sum()), int((~m1).sum()) + T = _staged_totals(adata, n_staged) + p1 = rng.normal(center, sd, size=(n1, n_staged)) + hi = rng.random((n2, n_staged)) < 0.5 + p2 = rng.normal(np.where(hi, center + sep, center - sep), sd) + g1 = _match_block_totals(p1, T[m1], rng) + g2 = _match_block_totals(p2, T[~m1], rng) + + X = np.asarray(adata.X).copy().astype(np.int64) + X[m1, :n_staged], X[~m1, :n_staged] = g1, g2 + adata.X = X + adata.obs["group"] = adata.obs["group"].astype("category") + return adata, list(adata.var_names[:n_staged]) + + +def build_trimodal(sim_seed=32, stage_seed=0, n_staged=10, ncells=2000, + ngenes=2000, center=8.0, sd=1.5, sep=4.0, **_): + """group_1 normal, group_2 trimodal about the same center.""" + adata = _background(sim_seed, ncells, ngenes) + rng = np.random.default_rng(stage_seed) + m1 = (adata.obs["group"] == "group_1").values + n1, n2 = int(m1.sum()), int((~m1).sum()) + T = _staged_totals(adata, n_staged) + p1 = rng.normal(center, sd, size=(n1, n_staged)) + state = rng.integers(0, 3, size=(n2, n_staged)) + mu2 = np.select([state == 0, state == 1], + [center - sep, center], default=center + sep) + p2 = rng.normal(mu2, sd) + g1 = _match_block_totals(p1, T[m1], rng) + g2 = _match_block_totals(p2, T[~m1], rng) + + X = np.asarray(adata.X).copy().astype(np.int64) + X[m1, :n_staged], X[~m1, :n_staged] = g1, g2 + adata.X = X + adata.obs["group"] = adata.obs["group"].astype("category") + return adata, list(adata.var_names[:n_staged]) + + +def build_coexpression(sim_seed=32, stage_seed=0, n_staged=10, ncells=2000, + ngenes=2000, **_): + """Same marginals in both groups, staged genes correlated only in group_2.""" + adata = _background(sim_seed, ncells, ngenes) + rng = np.random.default_rng(stage_seed) + m1 = (adata.obs["group"] == "group_1").values + n1, n2 = int(m1.sum()), int((~m1).sum()) + lat1 = rng.negative_binomial(5, 0.5, size=(n1, n_staged)).astype(float) + lat2 = rng.negative_binomial(5, 0.5, size=(n2, n_staged)).astype(float) + # one shared cell order makes the group_2 genes rise together + order = np.argsort(rng.standard_normal(n2)) + for j in range(n_staged): + lat2[:, j] = np.sort(lat2[:, j])[order] + T = _staged_totals(adata, n_staged) + g1 = _match_block_totals(lat1, T[m1], rng) + g2 = _match_block_totals(lat2, T[~m1], rng) + + X = np.asarray(adata.X).copy().astype(np.int64) + X[m1, :n_staged] = np.asarray(g1).astype(np.int64) + X[~m1, :n_staged] = np.asarray(g2).astype(np.int64) + adata.X = X + adata.obs["group"] = adata.obs["group"].astype("category") + return adata, list(adata.var_names[:n_staged]) + + +def build_skew(sim_seed=32, stage_seed=0, n_staged=10, ncells=2000, + ngenes=2000, center=8.0, sd=2.0, alpha=6.0, **_): + """group_1 right-tailed, group_2 left-tailed, same mean.""" + adata = _background(sim_seed, ncells, ngenes) + rng = np.random.default_rng(stage_seed) + m1 = (adata.obs["group"] == "group_1").values + n1, n2 = int(m1.sum()), int((~m1).sum()) + T = _staged_totals(adata, n_staged) + + d = alpha / np.sqrt(1.0 + alpha ** 2) + shift = sd * d * np.sqrt(2.0 / np.pi) + + def draw(a_shape, shape): + loc = center - np.sign(a_shape) * shift + return skewnorm.rvs(a_shape, loc=loc, scale=sd, size=shape, + random_state=rng) + + g1 = _match_block_totals(draw(+alpha, (n1, n_staged)), T[m1], rng) + g2 = _match_block_totals(draw(-alpha, (n2, n_staged)), T[~m1], rng) + + X = np.asarray(adata.X).copy().astype(np.int64) + X[m1, :n_staged], X[~m1, :n_staged] = g1, g2 + adata.X = X + adata.obs["group"] = adata.obs["group"].astype("category") + return adata, list(adata.var_names[:n_staged]) + + +BUILDERS = {"null": build_null, "bimodal": build_bimodal, + "trimodal": build_trimodal, "skew": build_skew, + "coexpression": build_coexpression} + + +def _normalized(adata): + a = adata.copy() + sc.pp.normalize_total(a, target_sum=1e4) + sc.pp.log1p(a) + return a + + +def _filter_ranked_genes(adata, method, n_top=50, pval_cutoff=0.05): + """Top ranked genes with p < pval_cutoff, as (raw p panel, adjusted p panel). + + logreg has no p-values, so both panels are its top n_top genes. + """ + a = _normalized(adata) + sc.tl.rank_genes_groups(a, groupby="group", groups=["group_2"], + reference="group_1", method=method, use_raw=False) + rankings = a.uns["rank_genes_groups"] + names = np.asarray(rankings["names"]["group_2"]) + panels = [] + for key in ("pvals", "pvals_adj"): + if key not in rankings: + panels.append(names[:n_top].tolist()) + continue + pvals = np.asarray(rankings[key]["group_2"], dtype=float) + mask = np.isfinite(pvals) & (pvals < pval_cutoff) + panels.append(names[mask].tolist()[:n_top]) + return tuple(panels) + + +def run_wilcoxon(adata, n_top=50, pval_cutoff=0.05): + return _filter_ranked_genes(adata, "wilcoxon", n_top=n_top, + pval_cutoff=pval_cutoff) + + +def run_ttest(adata, n_top=50, pval_cutoff=0.05): + return _filter_ranked_genes(adata, "t-test", n_top=n_top, + pval_cutoff=pval_cutoff) + + +def run_logreg(adata, n_top=50, pval_cutoff=0.05): + return _filter_ranked_genes(adata, "logreg", n_top=n_top, + pval_cutoff=pval_cutoff) + + +def run_recursieve(adata, n_top=50, seed=42, max_iterations=50, pval_cutoff=0.05): + model = recursieve.recursieve( + adata.copy(), group1="group_1", group2="group_2", field_name="group", + max_iterations=max_iterations, flip_rate_percentage=0.01, seed=seed, + pval_cutoff=pval_cutoff, print_to_console=False, plots=False, + ) + return list(model.genes)[:n_top] + + +def count_recovered(panel, staged): + """Number of staged genes in the panel.""" + return int(sum(1 for g in panel if g in set(staged))) + + +_RAMP = ["#f4f7fb", "#dce7f3", "#bcd2e8", "#94b6d8", "#6b97c4", + "#4a79ac", "#325d8e", "#1f4370", "#132c4d"] +_INK, _MUTED, _SURFACE = "#1a1d21", "#5c636b", "#ffffff" + + +TITLE = "Genes Recovered" +SUBTITLE = "(Simulated)" +SUBTITLE_ADJ = "(Simulated, adjusted p)" +METHOD_LABELS = {"recursieve": "RecurSieve"} + +def _normalize_scenario_labels(df): + """Keep "null" as a scenario name instead of NA.""" + df = df.copy() + df["scenario"] = df["scenario"].astype("string") + df["scenario"] = df["scenario"].fillna("null") + df["scenario"] = df["scenario"].replace({"nan": "null", "NaN": "null", "None": "null", "": "null"}) + df["scenario"] = df["scenario"].astype(str) + return df + + +def _mixed_text(fig, parts, x, y, transform, vertical=False, **kw): + """Draw (text, style) pieces end to end as one centered label.""" + renderer = fig.canvas.get_renderer() + artists = [fig.text(0, 0, t, rotation=90 if vertical else 0, + ha="left", va="bottom", **{**kw, **st}) for t, st in parts] + sizes = [a.get_window_extent(renderer) for a in artists] + lengths = [b.height if vertical else b.width for b in sizes] + ax_px = transform.transform((x, y)) + start = (ax_px[1] if vertical else ax_px[0]) - sum(lengths) / 2 + for a, b, n in zip(artists, sizes, lengths): + px = (ax_px[0] - b.width / 2, start) if vertical else (start, ax_px[1]) + a.set_position(fig.transFigure.inverted().transform(px)) + start += n + + +def correction_note(pval_cutoff): + """Figure note on how the DE panels were filtered.""" + return (f"Wilcoxon and t-test panels: genes with Benjamini\u2013Hochberg adjusted " + f"p < {pval_cutoff:g} (scanpy rank_genes_groups). LogReg has no p-values " + "(top genes by score). RecurSieve does not filter on p-values.") + + +def plot_heatmap(df, out_dir, n_staged, scenarios, value="n_recovered", + subtitle=SUBTITLE, suffix="", tag="", note=None): + """Mean genes recovered per scenario and method. + + SEM is across seeds for one dataset, or across per-dataset means for several. + """ + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + from matplotlib.colors import LinearSegmentedColormap + + plt.rcParams.update({ + "font.family": "Arial", + "mathtext.fontset": "custom", "mathtext.rm": "Arial", + "mathtext.it": "Arial:italic", "mathtext.bf": "Arial:bold", + "pdf.fonttype": 42, "ps.fonttype": 42, + }) + df = _normalize_scenario_labels(df) + cols = [m for m in METHOD_ORDER if m in set(df["method"])] + ordered_scenarios = [s for s in SCENARIO_ORDER if s in scenarios] + missing = [s for s in scenarios if s not in ordered_scenarios] + ordered_scenarios.extend(missing) + n_ds = df["dataset"].nunique() + n_seeds = int(df.groupby(["scenario", "method", "dataset"]).size().max()) + if n_ds > 1: + sem_note = (f"Values are mean \u00b1 SEM across {n_ds} dataset means " + f"({n_seeds} RecurSieve seeds each).") + df = df.groupby(["scenario", "method", "dataset"], as_index=False)[value].mean() + else: + sem_note = (f"Values are mean \u00b1 SEM across {n_seeds} RecurSieve seeds on one " + "dataset; DE methods are deterministic, so their SEM is 0.") + note = f"{sem_note} {note}" if note else sem_note + piv = (df.pivot_table(index="scenario", columns="method", + values=value)[cols].reindex(ordered_scenarios, fill_value=0.0)) + M = np.nan_to_num(piv.to_numpy(dtype=float), nan=0.0) + sem = (df.pivot_table(index="scenario", columns="method", values=value, + aggfunc="sem")[cols].reindex(ordered_scenarios)) + S = np.nan_to_num(sem.to_numpy(dtype=float), nan=0.0) + + cmap = LinearSegmentedColormap.from_list("seq_blue", _RAMP) + fig, ax = plt.subplots(figsize=(max(7.5, 1.3 * M.shape[1] + 2.4), + 0.85 * M.shape[0] + 1.9)) + fig.subplots_adjust(left=0.22, right=0.86, top=0.88, bottom=0.29) + fig.patch.set_facecolor(_SURFACE); ax.set_facecolor(_SURFACE) + mesh = ax.pcolormesh(M, cmap=cmap, vmin=0, vmax=n_staged, + edgecolors=_SURFACE, linewidth=2) + for i in range(M.shape[0]): + for j in range(M.shape[1]): + v = M[i, j] + if np.isfinite(v): + label = "0.0" if abs(v) < 1e-12 else f"{v:.1f}" + color = _SURFACE if v > n_staged * 0.55 else _INK + ax.text(j + 0.5, i + 0.42, label, ha="center", va="center", + fontsize=8, color=color) + ax.text(j + 0.5, i + 0.68, f"\u00b1{S[i, j]:.2f}", ha="center", + va="center", fontsize=6, color=color) + + ax.set_xticks(np.arange(M.shape[1]) + 0.5) + ax.set_xticklabels([METHOD_LABELS.get(c, c) for c in cols], rotation=35, + ha="right", fontsize=14, color=_INK) + ax.set_yticks(np.arange(M.shape[0]) + 0.5) + ax.set_yticklabels(list(piv.index), fontsize=13, color=_INK) + ax.invert_yaxis() + for sp in ax.spines.values(): + sp.set_visible(False) + ax.tick_params(length=0) + + cbar = fig.colorbar(mesh, ax=ax, pad=0.02, fraction=0.04) + cbar.set_ticks(np.arange(0, n_staged + 1, 2)) + _mixed_text(fig, [("mean genes recovered ", {}), (f"(of {n_staged})", {"fontstyle": "italic"})], + 4.2, 0.5, cbar.ax.transAxes, vertical=True, fontsize=12, color=_INK) + cbar.outline.set_visible(False) + cbar.ax.tick_params(length=0, labelsize=15, colors=_INK) + + _mixed_text(fig, [(TITLE + " ", {"fontweight": "bold"}), (subtitle, {"fontstyle": "italic"})], + 0.5, 1.05, ax.transAxes, fontsize=14, color=_INK) + fig.text(0.5, 0.02, textwrap.fill(note, 125), ha="center", va="bottom", fontsize=8, + color=_MUTED) + for ext in ("png", "pdf"): + fig.savefig(out_dir / f"feature_comparison_wide{tag}_heatmap{suffix}.{ext}", + dpi=300, facecolor=_SURFACE) + plt.close(fig) + + +def ordered_scenarios(df, scenarios): + """Scenarios in SCENARIO_ORDER, then any others.""" + seen = list(dict.fromkeys(list(df["scenario"]) + scenarios)) + order = [x for x in SCENARIO_ORDER if x in seen] + return order + [x for x in seen if x not in order and x in BUILDERS] + +def main(): + ap = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--datasets", type=int, default=25, help="simulated datasets per scenario") + ap.add_argument("--seeds", type=int, default=25, help="RecurSieve seeds per dataset") + ap.add_argument("--first-dataset", type=int, default=0) + ap.add_argument("--scenarios", default="null,bimodal,trimodal,skew,coexpression") + ap.add_argument("--n-staged", type=int, default=10) + ap.add_argument("--n-top", type=int, default=50, help="panel size per method") + ap.add_argument("--pval-cutoff", type=float, default=0.05, + help="BH adjusted p cutoff for the DE methods") + ap.add_argument("--ncells", type=int, default=2000) + ap.add_argument("--ngenes", type=int, default=2000) + ap.add_argument("--sim-seed", type=int, default=32, help="dataset d uses sim_seed+d") + ap.add_argument("--stage-seed", type=int, default=0, help="dataset d uses stage_seed+d") + ap.add_argument("--rs-seed", type=int, default=42, help="seed i uses rs_seed+i") + ap.add_argument("--max-iterations", type=int, default=50) + ap.add_argument("--center", type=float, default=8.0) + ap.add_argument("--sd", type=float, default=1.5) + ap.add_argument("--sep", type=float, default=4.0, + help="bimodal/trimodal: distance from center to each mode") + ap.add_argument("--alpha", type=float, default=6.0, + help="skew: skew-normal shape; group_1 +alpha, group_2 -alpha") + ap.add_argument("--out-dir", default="feature_comparison_results") + ap.add_argument("--plot-only", action="store_true", + help="merge any array part files into the CSV and plot; run nothing") + ap.add_argument("--n-tasks", type=int, default=1, + help="number of array tasks sharing the chunks (with --block-index)") + ap.add_argument("--make-todo", action="store_true", + help="list the unfinished chunks of seeds in todo_blocks.csv") + ap.add_argument("--seed-chunk", type=int, default=5, + help="seeds per chunk for --make-todo") + ap.add_argument("--block-index", type=int, default=None, + help="with --n-tasks N, run chunks i, i+N, ... of todo_blocks.csv " + "(e.g. $SLURM_ARRAY_TASK_ID)") + args = ap.parse_args() + + scenarios = [x.strip() for x in args.scenarios.split(",")] + bad = [x for x in scenarios if x not in BUILDERS] + if bad: + raise SystemExit(f"unknown scenarios {bad}; choose from {sorted(BUILDERS)}") + + out_dir = Path(args.out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + csv = out_dir / "feature_comparison_wide_multi_counts.csv" + parts_dir = out_dir / "parts" + keys = ["dataset", "scenario", "seed", "method"] + + def read_counts(path): + if not path.exists(): + return pd.DataFrame(columns=keys + ["n_recovered", "panel_size", + "n_recovered_adj", "panel_size_adj"]) + return _normalize_scenario_labels(pd.read_csv(path, keep_default_na=False, na_values=[])) + + def tidy(df): + df = df.copy() + df["dataset"] = df["dataset"].astype(int) + df["seed"] = df["seed"].astype(int) + return df.drop_duplicates(subset=keys, keep="last").reset_index(drop=True) + + def complete(df, ds, scen): + rs = df[(df["dataset"].astype(int) == ds) & (df["scenario"] == scen) + & (df["method"] == "recursieve")] + return rs["seed"].astype(int).isin(range(args.seeds)).sum() == args.seeds + + def run_block(ds, scen, seeds=None): + """Run one dataset x scenario block, or some of its seeds: DE methods once, + RecurSieve per seed.""" + seeds = range(args.seeds) if seeds is None else seeds + t_block = time.time() + adata, staged = BUILDERS[scen]( + sim_seed=args.sim_seed + ds, stage_seed=args.stage_seed + ds, + n_staged=args.n_staged, ncells=args.ncells, ngenes=args.ngenes, + center=args.center, sd=args.sd, sep=args.sep, alpha=args.alpha) + check_staging(adata, staged) + # DE methods are deterministic: one run per dataset, reused for every seed + # each entry is (raw p panel, adjusted p panel) + de_panels = { + "Wilcoxon": run_wilcoxon(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), + "TTest": run_ttest(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), + "LogReg": run_logreg(adata, n_top=args.n_top, pval_cutoff=args.pval_cutoff), + } + rows = [] + for seed in seeds: + rs = run_recursieve(adata, n_top=args.n_top, seed=args.rs_seed + seed, + max_iterations=args.max_iterations, + pval_cutoff=args.pval_cutoff) + for m in METHOD_ORDER: + if m == "recursieve": + # recursieve does not filter on p, so raw and adjusted match + n = count_recovered(rs, staged) + n, size, n_adj, size_adj = n, len(rs), n, len(rs) + else: + raw, adj = de_panels[m] + n, size = count_recovered(raw, staged), len(raw) + n_adj, size_adj = count_recovered(adj, staged), len(adj) + rows.append(dict(dataset=ds, scenario=scen, seed=seed, method=m, + n_recovered=n, panel_size=size, + n_recovered_adj=n_adj, panel_size_adj=size_adj)) + rs_mean = np.mean([r["n_recovered_adj"] for r in rows if r["method"] == "recursieve"]) + print(f"[d{ds} {scen} seeds {seeds[0]}-{seeds[-1]}] RecurSieve mean {rs_mean:.2f} " + f"[{time.time() - t_block:.0f}s]", flush=True) + return rows + + todo_csv = out_dir / "todo_blocks.csv" + if args.make_todo: + # unfinished chunks of seeds, from the main CSV and every part file + parts = sorted(parts_dir.glob("*.csv")) + saved = pd.concat([read_counts(csv)] + [read_counts(f) for f in parts], ignore_index=True) + rs = saved[saved["method"] == "recursieve"] + have = set(zip(rs["dataset"].astype(int), rs["scenario"], rs["seed"].astype(int))) + datasets = range(args.first_dataset, args.first_dataset + args.datasets) + todo = [] + for d in datasets: + for sc_ in scenarios: + for start in range(0, args.seeds, args.seed_chunk): + end = min(start + args.seed_chunk, args.seeds) + if any((d, sc_, s_) not in have for s_ in range(start, end)): + todo.append((d, sc_, start, end)) + pd.DataFrame(todo, columns=["dataset", "scenario", "seed_start", "seed_end"]).to_csv( + todo_csv, index=False) + print(f"{len(todo)} chunks of up to {args.seed_chunk} seeds still to run, listed in {todo_csv}") + return + + if args.block_index is not None: + # array task i runs chunks i, i + n_tasks, i + 2 * n_tasks, ... of the list + # written by --make-todo, each into its own file + todo = pd.read_csv(todo_csv, keep_default_na=False, na_values=[]) + mine = todo.iloc[args.block_index::args.n_tasks] + parts_dir.mkdir(exist_ok=True) + print(f"Task {args.block_index + 1}/{args.n_tasks}: {len(mine)} chunks", flush=True) + for r in mine.itertuples(index=False): + ds, scen = int(r.dataset), str(r.scenario) + seeds = range(int(r.seed_start), int(r.seed_end)) + out_csv = parts_dir / f"block_d{ds:02d}_{scen}_s{seeds[0]:02d}-{seeds[-1]:02d}.csv" + if out_csv.exists(): + print(f"[d{ds} {scen} seeds {seeds[0]}-{seeds[-1]}] already done, skipping") + continue + print(f"Block: dataset {ds}, {scen}, seeds {seeds[0]}-{seeds[-1]} -> {out_csv}", + flush=True) + tidy(pd.DataFrame(run_block(ds, scen, seeds))).to_csv(out_csv, index=False) + print("Task finished; run with --plot-only to merge the parts and plot.") + return + + if args.plot_only: + # merge the array tasks' part files into the main CSV, then plot + parts = sorted(parts_dir.glob("*.csv")) + df = pd.concat([read_counts(csv)] + [read_counts(f) for f in parts], ignore_index=True) + if df.empty: + raise SystemExit(f"No benchmark data in {csv} or {parts_dir}") + df = tidy(df) + df.to_csv(csv, index=False) + print(f"Merged {len(parts)} part files into {csv}") + else: + # one job runs every dataset in turn, skipping blocks already in the CSV + df = read_counts(csv) + datasets = range(args.first_dataset, args.first_dataset + args.datasets) + for ds in datasets: + for scen in scenarios: + if complete(df, ds, scen): + print(f"[d{ds} {scen}] already done, skipping", flush=True) + continue + rows = run_block(ds, scen) + # save after each dataset and scenario so a crash loses little + df = tidy(pd.concat([df, pd.DataFrame(rows)], ignore_index=True)) + df.to_csv(csv, index=False) + + if df.empty: + raise SystemExit(f"No benchmark data in {csv}") + order = ordered_scenarios(df, scenarios) + n_ds = df["dataset"].nunique() + value = "n_recovered_adj" + subtitle = SUBTITLE_ADJ.replace(")", f", {n_ds} datasets)" if n_ds > 1 else ")") + piv = df.pivot_table(index="scenario", columns="method", values=value) + print(piv[[m for m in METHOD_ORDER if m in piv.columns]].reindex(order).round(2).to_string()) + plot_heatmap(df, out_dir, args.n_staged, order, value=value, subtitle=subtitle, + suffix="_adj", tag="_multi", note=correction_note(args.pval_cutoff)) + print(f"Wrote {csv} and {out_dir / 'feature_comparison_wide_multi_heatmap_adj.png'}") + +if __name__ == "__main__": + main() diff --git a/manuscript/robustness/Supplemental_Table_1.xlsx b/manuscript/robustness/Supplemental_Table_1.xlsx index 74f2352b2387526ec7e78b56a26045882105b9de..b6fae2d9e89b63064f9b75d6df0543502b79041e 100644 GIT binary patch delta 31048 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+observed,7,0.0369992055385313 +observed,8,0.0290829644762229 +observed,9,0.0347576892520712 observed,10,0.0343037112700034 observed,11,0.0271819316763137 -observed,12,0.03163659062535467 -observed,13,0.021336965157189875 -observed,14,0.02079786630348428 -observed,15,0.020372261945295653 -observed,16,0.01957780047667688 -observed,17,0.02417432754511406 -observed,18,0.033140392690954486 -observed,19,0.02139371240494836 -observed,20,0.02372034956304619 -observed,21,0.021365338781069117 -observed,22,0.016910679832028146 -observed,23,0.021109976166155942 -observed,24,0.01512314152763591 -observed,25,0.013846328453070026 -observed,26,0.01359096583815685 -observed,27,0.013619339462036091 -observed,28,0.01268300987402111 -observed,29,0.012768130745658836 -observed,30,0.014584042673930315 -observed,31,0.01052661445919873 -observed,32,0.011377823175575985 -observed,33,0.011548064918851436 -observed,34,0.013761207581432301 -observed,35,0.011576438542730678 -observed,36,0.01146294404721371 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zBS~gadV_xd2$2DN(_=)McS&dck7s{y$ItNjb0{E|OZhoLe!cU*PfzzB8lzt+)~{Rs pZ=rkt@!/null_batches/shuffles_-/ and a + finished batch is skipped on a rerun. + +Merge mode (--merge): + For each group, combines the saved shuffles with every batch, recomputes the + per-gene p-values (with a Benjamini-Hochberg p_adj column) and the + module-level p-values, and overwrites the group's CSVs. + +Usage: + python alzheimers_null_hpc.py --group microglia --first-shuffle 51 --n-shuffles 5 \\ + --data /path/to/alz.h5ad + python alzheimers_null_hpc.py --merge +""" + +from __future__ import annotations + +import argparse +import hashlib +import os +from pathlib import Path + +import anndata as ad +import numpy as np +import pandas as pd +import scanpy as sc +from joblib import Parallel, delayed +from scipy.stats import false_discovery_control + +import recursieve + +PREFIX = "alzheimers_coexpression_null_" +TABLES = ("runs", "flip_rates", "panels") +FIELD_NAME = "Disease.Group" +GROUPS = ("low", "high") +ALL_GROUPS = ["microglia", "astrocytes", "inhibitory", "excitatory", "all_cell_types"] +# cell selection used for each group's original run +SELECTION = { + "microglia": dict(cell_type="Microglia", n_cells=3964, balance_groups=True), + "astrocytes": dict(cell_type="Astrocytes", n_cells=7818, balance_groups=True), + "inhibitory": dict(cell_type="Inhibitory", n_cells=25000, balance_groups=True), + "excitatory": dict(cell_type="Excitatory", n_cells=25000, balance_groups=True), + "all_cell_types": dict(cell_type=None, n_cells=50000, balance_groups=False), +} + + +# fingerprint of each group's cell list in the original run (first 16 hex +# characters of sha256 over the selected cell names, one per line) +CELL_HASH = { + "microglia": "ef3e999a9f0246c5", + "astrocytes": "26d8d8162ef07897", + "inhibitory": "9643e3f9e2bee0a8", + "excitatory": "31645efb3cb7ef3d", + "all_cell_types": "324b656ffb064b25", +} + + +def select_cells(obs, cell_type=None, cell_type_key="Major_celltypes", n_cells=50000, + balance_groups=False, seed=0): + """Row positions of the chosen cells, in output order (same as alz_loading.py).""" + pos = np.arange(len(obs)) + if cell_type is not None: + pos = pos[(obs[cell_type_key] == cell_type).to_numpy()] + print(f"Kept {cell_type} cells only: {len(pos)}") + sub = obs.iloc[pos] + + if balance_groups: + rng = np.random.default_rng(seed) + keep = [] + for g in GROUPS: + cells = np.flatnonzero((sub[FIELD_NAME] == g).to_numpy()) + keep.append(rng.choice(cells, size=min(n_cells // 2, len(cells)), replace=False)) + print(f"Sampled {len(keep[-1])} {g} cells") + return pos[np.sort(np.concatenate(keep))] + + # same cells and order as sc.pp.subsample on the full data + light = ad.AnnData(obs=sub.reset_index(drop=True).assign(_row=np.arange(len(sub)))) + light = sc.pp.subsample(light, n_obs=min(n_cells, len(sub)), random_state=seed, copy=True) + return pos[light.obs["_row"].to_numpy()] + + +def load_alzheimers(path, **selection): + """Load the chosen cells with gene symbols as var_names (same as alz_loading.py).""" + print(f"Loading data from: {path} (backed)") + backed = ad.read_h5ad(path, backed="r") + order = select_cells(backed.obs, **selection) + # backed indexing needs sorted rows + rows = np.sort(order) + adata = backed[rows].to_memory() + backed.file.close() + adata = adata[np.searchsorted(rows, order)].copy() + # plain strings: some anndata/pandas versions keep this column categorical, + # and making duplicate names unique then fails + adata.var_names = adata.var["feature_name"].astype(str).to_numpy() + adata.var_names_make_unique() + return adata + + +def observed_panel(panels): + obs = panels[panels["run"] == "observed"].sort_values("rank") + return obs["gene"].tolist() + + +def read_tables(folder): + return {key: pd.read_csv(folder / f"{PREFIX}{key}.csv") for key in TABLES} + + +def run_batch(args): + group_dir = Path(args.group) + last = args.first_shuffle + args.n_shuffles - 1 + batch = group_dir / "null_batches" / f"shuffles_{args.first_shuffle}-{last}" + if (batch / f"{PREFIX}panels.csv").exists(): + print(f"{batch} already done, skipping") + return + saved = read_tables(group_dir) + saved_panel = observed_panel(saved["panels"]) + + adata = load_alzheimers(args.data, **SELECTION[args.group]) + cell_hash = hashlib.sha256("\n".join(adata.obs_names).encode()).hexdigest()[:16] + if cell_hash != CELL_HASH[args.group]: + raise SystemExit(f"{args.group}: selected cells differ from the original run " + f"({cell_hash} vs {CELL_HASH[args.group]})") + print(f"{args.group}: cells match the original run ({adata.n_obs} cells)") + + model_args = dict(adata=adata, group1="low", group2="high", field_name=FIELD_NAME, + plots=False, print_to_console=False, max_iterations=50) + if args.use_saved_panel: + # preprocess only; the panel comes from the saved results + model = recursieve.recursieve(**model_args, run=False) + model.preprocessing() + model.filter_groups() + genes = set(pd.read_csv(group_dir / "alzheimers_de_full.csv")["gene"]) + if set(model.adata.var_names) != genes: + raise SystemExit(f"{args.group}: preprocessing kept different genes than " + "the original run (alzheimers_de_full.csv)") + print(f"{args.group}: preprocessed genes match the original run; " + f"using the saved panel ({len(saved_panel)} genes)") + panel = saved_panel + else: + model = recursieve.recursieve(**model_args) + if list(model.genes) != saved_panel: + raise SystemExit(f"{args.group}: observed panel differs from the saved one; " + "rerun with --use-saved-panel") + print(f"{args.group}: observed panel matches the saved one ({len(saved_panel)} genes)") + panel = list(model.genes) + + # same setup as recursieve.coexpression_null: shuffled panels match the + # observed length, with stopping rules off + X = model._get_X_dense(ad.AnnData(X=model.adata.layers["counts"])) + obs = model.adata.obs[[model.field_name]].copy() + params = dict(model._params, plots=False, print_to_console=False, + preprocess=False, run=False, flip_rate_percentage=-1.0, + patience=None, max_iterations=len(panel) - 1) + cores = len(os.sched_getaffinity(0)) + n_parallel = max(1, min(args.n_parallel, args.n_shuffles)) + params["n_jobs"] = max(1, cores // n_parallel) + shuffles = range(args.first_shuffle, last + 1) + print(f"Running shuffles {args.first_shuffle}-{last}, {n_parallel} at a time " + f"on {cores} cores", flush=True) + results = Parallel(n_jobs=n_parallel, verbose=10)( + delayed(recursieve.recursieve._run_null_shuffle)( + X, obs, model.adata.var_names, params, (model.seed, k), panel) + for k in shuffles + ) + + # the observed run's rows, as saved by the original run + rows = saved["runs"][saved["runs"]["run"] == "observed"].to_dict("records") + curves = [tuple(r) for r in saved["flip_rates"][saved["flip_rates"]["run"] == "observed"] + [["run", "iteration", "flip_rate"]].itertuples(index=False)] + panels = [("observed", i + 1, g) for i, g in enumerate(panel)] + for k, (summary, flips, genes) in zip(shuffles, results): + name = f"shuffle_{k}" + rows.append({"run": name, **summary}) + curves += [(name, i + 2, f) for i, f in enumerate(flips)] + panels += [(name, i + 1, g) for i, g in enumerate(genes)] + batch.mkdir(parents=True, exist_ok=True) + pd.DataFrame(rows).to_csv(batch / f"{PREFIX}runs.csv", index=False) + pd.DataFrame(curves, columns=["run", "iteration", "flip_rate"]).to_csv( + batch / f"{PREFIX}flip_rates.csv", index=False) + # panels last: its presence marks the batch as finished + pd.DataFrame(panels, columns=["run", "rank", "gene"]).to_csv( + batch / f"{PREFIX}panels.csv", index=False) + print(f"Batch written to {batch}") + + +def merge_group(group): + group_dir = Path(group) + saved = read_tables(group_dir) + panel = observed_panel(saved["panels"]) + batches = sorted(b for b in (group_dir / "null_batches").glob("*/") + if (b / f"{PREFIX}panels.csv").exists()) + merged = {key: [saved[key]] for key in TABLES} + for batch in batches: + tables = read_tables(batch) + if observed_panel(tables["panels"]) != panel: + raise SystemExit(f"{group}/{batch.name}: observed panel differs from the saved one") + for key in TABLES: + df = tables[key] + merged[key].append(df[df["run"] != "observed"]) + + # a shuffle already in the saved files is not counted twice + runs = pd.concat(merged["runs"], ignore_index=True).drop_duplicates("run", keep="first") + flips = pd.concat(merged["flip_rates"], ignore_index=True).drop_duplicates(["run", "iteration"]) + panels = pd.concat(merged["panels"], ignore_index=True).drop_duplicates(["run", "rank"]) + + freq = recursieve.recursieve._null_gene_frequency(panels) + freq["p_adj"] = false_discovery_control(freq["p_value"], method="bh") + pvalues = recursieve.recursieve._null_pvalues(runs) + outputs = {"runs": runs, "flip_rates": flips, "panels": panels, + "gene_frequency": freq, "pvalues": pvalues} + for key, df in outputs.items(): + df.to_csv(group_dir / f"{PREFIX}{key}.csv", index=False) + n = int(runs["run"].ne("observed").sum()) + print(f"{group}: {n} shuffles, {int((freq['p_adj'] < 0.05).sum())}/{len(freq)} genes " + f"with p_adj < 0.05") + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--group", choices=ALL_GROUPS) + ap.add_argument("--first-shuffle", type=int) + ap.add_argument("--n-shuffles", type=int, default=5) + ap.add_argument("--n-parallel", type=int, default=2, + help="shuffles run at once; each gets cores / n-parallel cores") + ap.add_argument("--data", help="path to alz.h5ad") + ap.add_argument("--use-saved-panel", action="store_true", + help="skip refitting the observed panel; use the saved one") + ap.add_argument("--merge", action="store_true", + help="combine the saved shuffles with every batch, for all groups") + args = ap.parse_args() + + if args.merge: + for group in ALL_GROUPS: + merge_group(group) + return + if not (args.group and args.first_shuffle and args.data): + ap.error("run mode needs --group, --first-shuffle and --data") + run_batch(args) + + +if __name__ == "__main__": + main() diff --git a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_flip_rates.csv index e07e52c..aedcf43 100644 --- a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_flip_rates.csv +++ b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_flip_rates.csv @@ -1,418 +1,1618 @@ run,iteration,flip_rate -observed,2,0.13213097979022767 -observed,3,0.09119979534407777 -observed,4,0.29994883601944233 -observed,5,0.02276797134817089 +observed,2,0.1321309797902276 +observed,3,0.0911997953440777 +observed,4,0.2999488360194423 +observed,5,0.0227679713481708 observed,6,0.0367101560501407 -observed,7,0.023279611153747763 -observed,8,0.01918649270913277 -observed,9,0.028907649015093375 -observed,10,0.020337682271680736 -observed,11,0.019570222563315427 -observed,12,0.016244563827065746 -observed,13,0.015605014070094653 -observed,14,0.01675620363264262 +observed,7,0.0232796111537477 +observed,8,0.0191864927091327 +observed,9,0.0289076490150933 +observed,10,0.0203376822716807 +observed,11,0.0195702225633154 +observed,12,0.0162445638270657 +observed,13,0.0156050140700946 +observed,14,0.0167562036326426 observed,15,0.0117677155282681 -observed,16,0.010232796111537478 -observed,17,0.010616525965720132 -observed,18,0.00908160654898951 -shuffle_1,2,0.05154771041187004 -shuffle_1,3,0.051675620363264264 -shuffle_1,4,0.04886160143259145 -shuffle_1,5,0.04579176259913021 -shuffle_1,6,0.03607060629316961 -shuffle_1,7,0.034791506779227425 -shuffle_1,8,0.03376822716807368 -shuffle_1,9,0.036454336147352266 -shuffle_1,10,0.03274494755691993 +observed,16,0.0102327961115374 +observed,17,0.0106165259657201 +observed,18,0.0090816065489895 +shuffle_1,2,0.05154771041187 +shuffle_1,3,0.0516756203632642 +shuffle_1,4,0.0488616014325914 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a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_gene_frequency.csv b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_gene_frequency.csv index 6949348..949b4c5 100644 --- a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_gene_frequency.csv +++ b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_gene_frequency.csv @@ -1,19 +1,19 @@ -gene,rank,n_shuffled_panels,shuffled_frequency,p_value -NEAT1,1,1,0.04,0.07692307692307693 -LINC00486,2,0,0.0,0.038461538461538464 -SLC1A2,3,0,0.0,0.038461538461538464 -DPP10,4,0,0.0,0.038461538461538464 -ATP1A2,5,0,0.0,0.038461538461538464 -LUC7L3,6,1,0.04,0.07692307692307693 -PNISR,7,0,0.0,0.038461538461538464 -DLC1,8,0,0.0,0.038461538461538464 -MIR9-1HG,9,0,0.0,0.038461538461538464 -GLUL,10,0,0.0,0.038461538461538464 -SYNE1,11,0,0.0,0.038461538461538464 -COL5A3,12,1,0.04,0.07692307692307693 -MGAT4C,13,0,0.0,0.038461538461538464 -FGFR3,14,0,0.0,0.038461538461538464 -HPSE2,15,0,0.0,0.038461538461538464 -NDRG2,16,0,0.0,0.038461538461538464 -TENM2,17,0,0.0,0.038461538461538464 -RBM25,18,2,0.08,0.11538461538461539 +gene,rank,n_shuffled_panels,shuffled_frequency,p_value,p_adj +NEAT1,1,3,0.03,0.039603960396039604,0.039603960396039604 +LINC00486,2,1,0.01,0.019801980198019802,0.02545968882602546 +SLC1A2,3,1,0.01,0.019801980198019802,0.02545968882602546 +DPP10,4,1,0.01,0.019801980198019802,0.02545968882602546 +ATP1A2,5,1,0.01,0.019801980198019802,0.02545968882602546 +LUC7L3,6,1,0.01,0.019801980198019802,0.02545968882602546 +PNISR,7,1,0.01,0.019801980198019802,0.02545968882602546 +DLC1,8,0,0.0,0.009900990099009901,0.02545968882602546 +MIR9-1HG,9,1,0.01,0.019801980198019802,0.02545968882602546 +GLUL,10,1,0.01,0.019801980198019802,0.02545968882602546 +SYNE1,11,1,0.01,0.019801980198019802,0.02545968882602546 +COL5A3,12,3,0.03,0.039603960396039604,0.039603960396039604 +MGAT4C,13,0,0.0,0.009900990099009901,0.02545968882602546 +FGFR3,14,0,0.0,0.009900990099009901,0.02545968882602546 +HPSE2,15,0,0.0,0.009900990099009901,0.02545968882602546 +NDRG2,16,0,0.0,0.009900990099009901,0.02545968882602546 +TENM2,17,2,0.02,0.0297029702970297,0.03341584158415842 +RBM25,18,2,0.02,0.0297029702970297,0.03341584158415842 diff --git a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_panels.csv index f17b88b..6232c63 100644 --- a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_panels.csv +++ b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_panels.csv @@ -467,3 +467,1353 @@ shuffle_25,15,NKAIN1 shuffle_25,16,VSNL1 shuffle_25,17,ENSG00000235939 shuffle_25,18,RHPN2P1 +shuffle_26,1,MYB +shuffle_26,2,ST8SIA3 +shuffle_26,3,LINC03048 +shuffle_26,4,ELAVL2 +shuffle_26,5,COL5A3 +shuffle_26,6,SHISA9 +shuffle_26,7,ENSG00000282381 +shuffle_26,8,NR1H4 +shuffle_26,9,ENSG00000287430 +shuffle_26,10,BORCS6 +shuffle_26,11,CACNA1A +shuffle_26,12,ENSG00000272256 +shuffle_26,13,SRSF11 +shuffle_26,14,PLPPR5-AS1 +shuffle_26,15,RMI2 +shuffle_26,16,DNAJB1 +shuffle_26,17,NRXN3 +shuffle_26,18,FAM111A-DT +shuffle_27,1,ENSG00000267053 +shuffle_27,2,ENSG00000254718 +shuffle_27,3,LINC02742 +shuffle_27,4,PET100 +shuffle_27,5,LINC02607 +shuffle_27,6,LINC02306 +shuffle_27,7,PBXIP1 +shuffle_27,8,LINC01297 +shuffle_27,9,MUC19 +shuffle_27,10,SINHCAFP3 +shuffle_27,11,RGSL1 +shuffle_27,12,ENSG00000219553 +shuffle_27,13,LENG9 +shuffle_27,14,MYBL2 +shuffle_27,15,ENSG00000226868 +shuffle_27,16,SYT1 +shuffle_27,17,CWF19L2 +shuffle_27,18,ENSG00000286229 +shuffle_28,1,ZNF804B +shuffle_28,2,CCDC144BP +shuffle_28,3,ABCA13 +shuffle_28,4,DLGAP1 +shuffle_28,5,KC6 +shuffle_28,6,ENSG00000256237 +shuffle_28,7,DRAXIN +shuffle_28,8,LINC02505 +shuffle_28,9,SCHLAP1 +shuffle_28,10,COL5A3 +shuffle_28,11,LINC00620 +shuffle_28,12,ABCC3 +shuffle_28,13,LINC02194 +shuffle_28,14,NRXN2 +shuffle_28,15,MTMR8 +shuffle_28,16,ACP7 +shuffle_28,17,B3GNT6 +shuffle_28,18,TRNP1 +shuffle_29,1,ENSG00000248242 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b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_pvalues.csv @@ -1,3 +1,3 @@ statistic,observed,null_mean,null_min,null_max,n_shuffles,p_value -mean_later_auc,0.9607275465909474,0.5051745225248441,0.4938367276485434,0.529241543767446,25,0.038461538461538464 -first_auc,0.9297543695931985,0.5018407199790106,0.4810146453777775,0.5318195924353655,25,0.038461538461538464 +mean_later_auc,0.9607275465909474,0.5056578533703988,0.4938367276485434,0.529241543767446,100,0.009900990099009901 +first_auc,0.9297543695931983,0.5000216580942767,0.4721108429286685,0.5318195924353655,100,0.009900990099009901 diff --git a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_runs.csv index f943262..0dfd4e3 100644 --- a/manuscript/robustness/astrocytes/alzheimers_coexpression_null_runs.csv +++ b/manuscript/robustness/astrocytes/alzheimers_coexpression_null_runs.csv @@ -1,27 +1,102 @@ 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a/manuscript/robustness/astrocytes/null_batches/shuffles_26-50/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_26-50/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..9868fe2 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_26-50/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,27 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607275465909474,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_26,MYB,0.4993448063090465,0.5162779104724539,18,max_iterations,0.024047070862113072,0.22013302634944998,0.02857142857142857 +shuffle_27,ENSG00000267053,0.4997002025540562,0.5014790249578953,18,max_iterations,0.02507035047326682,0.23356357124584293,0.0 +shuffle_28,ZNF804B,0.5085771140807411,0.505045832783054,18,max_iterations,0.017139933486825274,0.250575594781274,0.02857142857142857 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file mode 100644 index 0000000..a7265ef --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_51-55/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,98 @@ +run,iteration,flip_rate +observed,2,0.13213097979022767 +observed,3,0.09119979534407777 +observed,4,0.29994883601944233 +observed,5,0.02276797134817089 +observed,6,0.0367101560501407 +observed,7,0.023279611153747763 +observed,8,0.01918649270913277 +observed,9,0.028907649015093375 +observed,10,0.020337682271680736 +observed,11,0.019570222563315427 +observed,12,0.016244563827065746 +observed,13,0.015605014070094653 +observed,14,0.01675620363264262 +observed,15,0.0117677155282681 +observed,16,0.010232796111537478 +observed,17,0.010616525965720132 +observed,18,0.00908160654898951 +shuffle_51,2,0.051931440266052696 +shuffle_51,3,0.04860578152980302 +shuffle_51,4,0.04591967255052443 +shuffle_51,5,0.044896392939370686 +shuffle_51,6,0.042082374008697875 +shuffle_51,7,0.04425684318239959 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b/manuscript/robustness/astrocytes/null_batches/shuffles_51-55/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,109 @@ +run,rank,gene +observed,1,NEAT1 +observed,2,LINC00486 +observed,3,SLC1A2 +observed,4,DPP10 +observed,5,ATP1A2 +observed,6,LUC7L3 +observed,7,PNISR +observed,8,DLC1 +observed,9,MIR9-1HG +observed,10,GLUL +observed,11,SYNE1 +observed,12,COL5A3 +observed,13,MGAT4C +observed,14,FGFR3 +observed,15,HPSE2 +observed,16,NDRG2 +observed,17,TENM2 +observed,18,RBM25 +shuffle_51,1,ATP8A2 +shuffle_51,2,MAPK11 +shuffle_51,3,LINC01854 +shuffle_51,4,ENSG00000289349 +shuffle_51,5,ENSG00000217334 +shuffle_51,6,RHPN2P1 +shuffle_51,7,LBX1-AS1 +shuffle_51,8,ENSG00000274363 +shuffle_51,9,NLRP5 +shuffle_51,10,ENSG00000287860 +shuffle_51,11,ZNF385D +shuffle_51,12,ENSG00000276136 +shuffle_51,13,ENSG00000227017 +shuffle_51,14,ANK1 +shuffle_51,15,NYAP2 +shuffle_51,16,CLNK +shuffle_51,17,NEFM +shuffle_51,18,ID2 +shuffle_52,1,PSMA6 +shuffle_52,2,MARCHF6-DT +shuffle_52,3,SPAG5 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b/manuscript/robustness/astrocytes/null_batches/shuffles_56-60/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..c188378 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_56-60/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,98 @@ +run,iteration,flip_rate +observed,2,0.13213097979022767 +observed,3,0.09119979534407777 +observed,4,0.29994883601944233 +observed,5,0.02276797134817089 +observed,6,0.0367101560501407 +observed,7,0.023279611153747763 +observed,8,0.01918649270913277 +observed,9,0.028907649015093375 +observed,10,0.020337682271680736 +observed,11,0.019570222563315427 +observed,12,0.016244563827065746 +observed,13,0.015605014070094653 +observed,14,0.01675620363264262 +observed,15,0.0117677155282681 +observed,16,0.010232796111537478 +observed,17,0.010616525965720132 +observed,18,0.00908160654898951 +shuffle_56,2,0.051419800460475826 +shuffle_56,3,0.0498848810437452 +shuffle_56,4,0.04182655410590944 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/dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_56-60/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,109 @@ +run,rank,gene +observed,1,NEAT1 +observed,2,LINC00486 +observed,3,SLC1A2 +observed,4,DPP10 +observed,5,ATP1A2 +observed,6,LUC7L3 +observed,7,PNISR +observed,8,DLC1 +observed,9,MIR9-1HG +observed,10,GLUL +observed,11,SYNE1 +observed,12,COL5A3 +observed,13,MGAT4C +observed,14,FGFR3 +observed,15,HPSE2 +observed,16,NDRG2 +observed,17,TENM2 +observed,18,RBM25 +shuffle_56,1,DKK2 +shuffle_56,2,FAM111A-DT +shuffle_56,3,LINC00377 +shuffle_56,4,HSP90AB2P +shuffle_56,5,ENSG00000251680 +shuffle_56,6,LINC01847 +shuffle_56,7,LINC02669 +shuffle_56,8,ENSG00000233538 +shuffle_56,9,C2CD4D-AS1 +shuffle_56,10,FUS +shuffle_56,11,SGSM1 +shuffle_56,12,MIR4435-2HG +shuffle_56,13,ENSG00000265055 +shuffle_56,14,PPEF2 +shuffle_56,15,ENSG00000272449 +shuffle_56,16,MAP2K5-DT +shuffle_56,17,CRNDE +shuffle_56,18,ARL15 +shuffle_57,1,ENSG00000237760 +shuffle_57,2,ARRDC5 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b/manuscript/robustness/astrocytes/null_batches/shuffles_56-60/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_56,DKK2,0.49836946528490933,0.5078722560518569,18,max_iterations,0.02519826042466104,0.22819135328728576,0.0 +shuffle_57,ENSG00000237760,0.49892036580375876,0.5130309441492472,18,max_iterations,0.019058582757738553,0.2075978511128166,0.0 +shuffle_58,TMPRSS15,0.5057441190642831,0.5030519704125597,18,max_iterations,0.01816321309797902,0.21284215911997953,0.0 +shuffle_59,ABCG8,0.4959950695130345,0.5100833191779066,18,max_iterations,0.02673317984139166,0.08531593757994373,0.0 +shuffle_60,HYAL3,0.4861050243144814,0.5124733505155448,18,max_iterations,0.023151701202353544,0.22333077513430544,0.0 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_61-65/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_61-65/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..4769694 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_61-65/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,98 @@ +run,iteration,flip_rate +observed,2,0.13213097979022767 +observed,3,0.09119979534407777 +observed,4,0.29994883601944233 +observed,5,0.02276797134817089 +observed,6,0.0367101560501407 +observed,7,0.023279611153747763 +observed,8,0.01918649270913277 +observed,9,0.028907649015093375 +observed,10,0.020337682271680736 +observed,11,0.019570222563315427 +observed,12,0.016244563827065746 +observed,13,0.015605014070094653 +observed,14,0.01675620363264262 +observed,15,0.0117677155282681 +observed,16,0.010232796111537478 +observed,17,0.010616525965720132 +observed,18,0.00908160654898951 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b/manuscript/robustness/astrocytes/null_batches/shuffles_61-65/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..380c2b3 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_61-65/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,109 @@ +run,rank,gene +observed,1,NEAT1 +observed,2,LINC00486 +observed,3,SLC1A2 +observed,4,DPP10 +observed,5,ATP1A2 +observed,6,LUC7L3 +observed,7,PNISR +observed,8,DLC1 +observed,9,MIR9-1HG +observed,10,GLUL +observed,11,SYNE1 +observed,12,COL5A3 +observed,13,MGAT4C +observed,14,FGFR3 +observed,15,HPSE2 +observed,16,NDRG2 +observed,17,TENM2 +observed,18,RBM25 +shuffle_61,1,MIR325HG +shuffle_61,2,COL4A4 +shuffle_61,3,KCNJ3 +shuffle_61,4,PTPRC +shuffle_61,5,GLIS1 +shuffle_61,6,SLC8A1-AS1 +shuffle_61,7,SLTM +shuffle_61,8,CDK11A +shuffle_61,9,ADAMTS5 +shuffle_61,10,ENSG00000287704 +shuffle_61,11,PROSER2-AS1 +shuffle_61,12,ZNF689 +shuffle_61,13,ENSG00000257997 +shuffle_61,14,CASC9 +shuffle_61,15,STSP1 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index 0000000..a1412ec --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_61-65/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_61,MIR325HG,0.5058582237891636,0.5024898598050304,18,max_iterations,0.0200818623688923,0.22486569455103608,0.0 +shuffle_62,PPP1R3E,0.5022759168532679,0.5027536411140101,18,max_iterations,0.020337682271680736,0.23714504988488105,0.0 +shuffle_63,CDH13,0.48227561160495924,0.5050727788008471,18,max_iterations,0.021233051931440265,0.371194678946022,0.0 +shuffle_64,SLC22A9,0.5186960955107422,0.5072966229593421,18,max_iterations,0.02264006139677667,0.2509593246354566,0.0 +shuffle_65,CTSH,0.4957922974223234,0.5084258077930619,18,max_iterations,0.02430289076490151,0.23727295983627525,0.02857142857142857 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_66-70/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_66-70/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..4e217af --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_66-70/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,98 @@ +run,iteration,flip_rate +observed,2,0.13213097979022767 +observed,3,0.09119979534407777 +observed,4,0.29994883601944233 +observed,5,0.02276797134817089 +observed,6,0.0367101560501407 +observed,7,0.023279611153747763 +observed,8,0.01918649270913277 +observed,9,0.028907649015093375 +observed,10,0.020337682271680736 +observed,11,0.019570222563315427 +observed,12,0.016244563827065746 +observed,13,0.015605014070094653 +observed,14,0.01675620363264262 +observed,15,0.0117677155282681 +observed,16,0.010232796111537478 +observed,17,0.010616525965720132 +observed,18,0.00908160654898951 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b/manuscript/robustness/astrocytes/null_batches/shuffles_66-70/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..c510b7b --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_66-70/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,109 @@ +run,rank,gene +observed,1,NEAT1 +observed,2,LINC00486 +observed,3,SLC1A2 +observed,4,DPP10 +observed,5,ATP1A2 +observed,6,LUC7L3 +observed,7,PNISR +observed,8,DLC1 +observed,9,MIR9-1HG +observed,10,GLUL +observed,11,SYNE1 +observed,12,COL5A3 +observed,13,MGAT4C +observed,14,FGFR3 +observed,15,HPSE2 +observed,16,NDRG2 +observed,17,TENM2 +observed,18,RBM25 +shuffle_66,1,RPUSD1 +shuffle_66,2,TMEM238 +shuffle_66,3,ENSG00000258881 +shuffle_66,4,MSR1 +shuffle_66,5,LCP1 +shuffle_66,6,GUCY2D +shuffle_66,7,DKK2 +shuffle_66,8,ENSG00000239572 +shuffle_66,9,MIR7515HG +shuffle_66,10,LINC01117 +shuffle_66,11,ENSG00000227743 +shuffle_66,12,CHRM3 +shuffle_66,13,MDGA2 +shuffle_66,14,KCNJ6 +shuffle_66,15,TPD52L2 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file mode 100644 index 0000000..1502165 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_66-70/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_66,RPUSD1,0.4923190077394982,0.5102343726609578,18,max_iterations,0.016116653875671526,0.016116653875671526,0.0 +shuffle_67,SYCP2,0.5049083201240762,0.5061831201249962,18,max_iterations,0.017779483243796367,0.2339473011000256,0.0 +shuffle_68,ENSG00000236892,0.502410371465388,0.5044505419724484,18,max_iterations,0.02072141212586339,0.20542338193911486,0.0 +shuffle_69,LDB2,0.48936464018128845,0.5009393694203702,18,max_iterations,0.022128421591199796,0.022128421591199796,0.0 +shuffle_70,CSPP1,0.5031429674060217,0.5080896786581499,18,max_iterations,0.02174469173701714,0.18751598874392428,0.0 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..e8fd08e --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,98 @@ +run,iteration,flip_rate +observed,2,0.13213097979022767 +observed,3,0.09119979534407777 +observed,4,0.29994883601944233 +observed,5,0.02276797134817089 +observed,6,0.0367101560501407 +observed,7,0.023279611153747763 +observed,8,0.01918649270913277 +observed,9,0.028907649015093375 +observed,10,0.020337682271680736 +observed,11,0.019570222563315427 +observed,12,0.016244563827065746 +observed,13,0.015605014070094653 +observed,14,0.01675620363264262 +observed,15,0.0117677155282681 +observed,16,0.010232796111537478 +observed,17,0.010616525965720132 +observed,18,0.00908160654898951 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b/manuscript/robustness/astrocytes/null_batches/shuffles_71-75/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..f95d316 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_71-75/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,109 @@ +run,rank,gene +observed,1,NEAT1 +observed,2,LINC00486 +observed,3,SLC1A2 +observed,4,DPP10 +observed,5,ATP1A2 +observed,6,LUC7L3 +observed,7,PNISR +observed,8,DLC1 +observed,9,MIR9-1HG +observed,10,GLUL +observed,11,SYNE1 +observed,12,COL5A3 +observed,13,MGAT4C +observed,14,FGFR3 +observed,15,HPSE2 +observed,16,NDRG2 +observed,17,TENM2 +observed,18,RBM25 +shuffle_71,1,ENSG00000249881 +shuffle_71,2,TCEAL6 +shuffle_71,3,LINC02015 +shuffle_71,4,ENSG00000257449 +shuffle_71,5,CDK11B +shuffle_71,6,LINC00926 +shuffle_71,7,ENSG00000287332 +shuffle_71,8,BARX2 +shuffle_71,9,RSPH10B2 +shuffle_71,10,PRKCB +shuffle_71,11,NMNAT2 +shuffle_71,12,ENSG00000251567 +shuffle_71,13,DDN +shuffle_71,14,ENSG00000258938 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file mode 100644 index 0000000..2494755 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_71-75/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_71,ENSG00000249881,0.4843527536667953,0.5040122624108718,18,max_iterations,0.021488871834228703,0.023663341007930418,0.0 +shuffle_72,ENSG00000239572,0.4923895055631504,0.5100335399447674,18,max_iterations,0.0249424405218726,0.21220260936300844,0.0 +shuffle_73,FCN1,0.5264341401336843,0.5071263389081175,18,max_iterations,0.01650038372985418,0.01650038372985418,0.0 +shuffle_74,TRIM36,0.478759441802593,0.5090542203668464,18,max_iterations,0.022128421591199796,0.024430800716295727,0.02857142857142857 +shuffle_75,GABRB2,0.4891131737175392,0.5101669903193662,18,max_iterations,0.017523663341007932,0.021872601688411358,0.0 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..ad785e1 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,98 @@ +run,iteration,flip_rate +observed,2,0.13213097979022767 +observed,3,0.09119979534407777 +observed,4,0.29994883601944233 +observed,5,0.02276797134817089 +observed,6,0.0367101560501407 +observed,7,0.023279611153747763 +observed,8,0.01918649270913277 +observed,9,0.028907649015093375 +observed,10,0.020337682271680736 +observed,11,0.019570222563315427 +observed,12,0.016244563827065746 +observed,13,0.015605014070094653 +observed,14,0.01675620363264262 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a/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..7c0dd2e --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,109 @@ +run,rank,gene +observed,1,NEAT1 +observed,2,LINC00486 +observed,3,SLC1A2 +observed,4,DPP10 +observed,5,ATP1A2 +observed,6,LUC7L3 +observed,7,PNISR +observed,8,DLC1 +observed,9,MIR9-1HG +observed,10,GLUL +observed,11,SYNE1 +observed,12,COL5A3 +observed,13,MGAT4C +observed,14,FGFR3 +observed,15,HPSE2 +observed,16,NDRG2 +observed,17,TENM2 +observed,18,RBM25 +shuffle_76,1,LINC00624 +shuffle_76,2,ABHD15 +shuffle_76,3,DDN +shuffle_76,4,LINC00685 +shuffle_76,5,MARCHF1 +shuffle_76,6,ENSG00000253642 +shuffle_76,7,LINC02395 +shuffle_76,8,LINC02377 +shuffle_76,9,ENSG00000287704 +shuffle_76,10,ENSG00000230021 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+shuffle_78,18,CCDC34 +shuffle_79,1,DGKI +shuffle_79,2,PYDC2-AS1 +shuffle_79,3,AHR +shuffle_79,4,SRRT +shuffle_79,5,GALNT6 +shuffle_79,6,KCNIP4 +shuffle_79,7,LINC02238 +shuffle_79,8,SAFB2 +shuffle_79,9,MAGEA3-DT +shuffle_79,10,GRIK3 +shuffle_79,11,IQANK1 +shuffle_79,12,SMTN +shuffle_79,13,CCDC144CP +shuffle_79,14,SND1-DT +shuffle_79,15,LINC01010 +shuffle_79,16,LINC01721 +shuffle_79,17,KCNC2 +shuffle_79,18,CACNA1E +shuffle_80,1,CIRBP +shuffle_80,2,XKR4 +shuffle_80,3,FPR1 +shuffle_80,4,CCNT1 +shuffle_80,5,ENSG00000254303 +shuffle_80,6,B3GALT6 +shuffle_80,7,BRS3 +shuffle_80,8,IGFBP4 +shuffle_80,9,SMIM43 +shuffle_80,10,ENSG00000287823 +shuffle_80,11,RRAD +shuffle_80,12,ENSG00000287416 +shuffle_80,13,PSMA6 +shuffle_80,14,NUDT17 +shuffle_80,15,AEBP1 +shuffle_80,16,ENSG00000267627 +shuffle_80,17,ELF4 +shuffle_80,18,ENSG00000272942 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..b8da066 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_76,LINC00624,0.5103853469183366,0.5072657971731538,18,max_iterations,0.024686620619084165,0.18789971859810695,0.0 +shuffle_77,LINC03000,0.5085909229327967,0.503693913191292,18,max_iterations,0.020593502174469175,0.21898183678690203,0.0 +shuffle_78,LINC02647,0.5028551618579157,0.505480278637085,18,max_iterations,0.02072141212586339,0.1931440266052699,0.0 +shuffle_79,DGKI,0.5138310188970506,0.504853533182313,18,max_iterations,0.01918649270913277,0.2304937324123817,0.0 +shuffle_80,CIRBP,0.5106833274100625,0.5067547721722777,18,max_iterations,0.024814530570478385,0.18163213097979022,0.0 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..9ec11d4 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,98 @@ +run,iteration,flip_rate +observed,2,0.13213097979022767 +observed,3,0.09119979534407777 +observed,4,0.29994883601944233 +observed,5,0.02276797134817089 +observed,6,0.0367101560501407 +observed,7,0.023279611153747763 +observed,8,0.01918649270913277 +observed,9,0.028907649015093375 +observed,10,0.020337682271680736 +observed,11,0.019570222563315427 +observed,12,0.016244563827065746 +observed,13,0.015605014070094653 +observed,14,0.01675620363264262 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a/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..53d0e55 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,109 @@ +run,rank,gene +observed,1,NEAT1 +observed,2,LINC00486 +observed,3,SLC1A2 +observed,4,DPP10 +observed,5,ATP1A2 +observed,6,LUC7L3 +observed,7,PNISR +observed,8,DLC1 +observed,9,MIR9-1HG +observed,10,GLUL +observed,11,SYNE1 +observed,12,COL5A3 +observed,13,MGAT4C +observed,14,FGFR3 +observed,15,HPSE2 +observed,16,NDRG2 +observed,17,TENM2 +observed,18,RBM25 +shuffle_81,1,SLC14A1 +shuffle_81,2,ENSG00000285876 +shuffle_81,3,EIF4E1B +shuffle_81,4,CDRT15P6 +shuffle_81,5,ZSWIM1 +shuffle_81,6,TEX26 +shuffle_81,7,LINC02984 +shuffle_81,8,AFF2 +shuffle_81,9,ZDHHC11B +shuffle_81,10,NPIPA3 +shuffle_81,11,PPP1R3E 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b/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..f2acad7 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_81-85/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_81,SLC14A1,0.4996435135824595,0.5106075724609992,18,max_iterations,0.02276797134817089,0.2202609363008442,0.02857142857142857 +shuffle_82,NPM2,0.4850017697134082,0.5081581487034887,18,max_iterations,0.017395753389613713,0.017395753389613713,0.0 +shuffle_83,SLC6A7,0.4866065036786055,0.5001218207923173,18,max_iterations,0.021872601688411358,0.23740086978766947,0.0 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+shuffle_90,18,PRR22 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..f9fd357 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_86,ANKRD20A9P,0.5051568794610768,0.5008517569457491,18,max_iterations,0.02519826042466104,0.22499360450243028,0.0 +shuffle_87,SLC22A10,0.4968744753544696,0.5005168358024416,18,max_iterations,0.019570222563315427,0.026093630084420567,0.0 +shuffle_88,CD44,0.5036626163123242,0.5060005873705089,18,max_iterations,0.017907393195190587,0.23497058071117932,0.0 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+shuffle_95,16,SMCO4 +shuffle_95,17,FAM178B +shuffle_95,18,BCORP1 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..7069376 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_91,ADAMTS9-AS2,0.5007860143946381,0.5101057340190122,18,max_iterations,0.019442312611921208,0.20887695062675876,0.0 +shuffle_92,EHF,0.518289824547633,0.5084848487597295,18,max_iterations,0.022000511639805577,0.19084164748017396,0.0 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+observed,18,RBM25 +shuffle_96,1,ENSG00000287968 +shuffle_96,2,ENSG00000267092 +shuffle_96,3,ENSG00000284977 +shuffle_96,4,ENSG00000263826 +shuffle_96,5,ENSG00000286046 +shuffle_96,6,SYT1 +shuffle_96,7,PGR +shuffle_96,8,CFAP20DC-DT +shuffle_96,9,ARL4D +shuffle_96,10,MRLN +shuffle_96,11,C2CD4D-AS1 +shuffle_96,12,CUX2 +shuffle_96,13,SMPD2 +shuffle_96,14,EEF1AKMT4-ECE2 +shuffle_96,15,ENSG00000242593 +shuffle_96,16,ENSG00000259283 +shuffle_96,17,ABCA9 +shuffle_96,18,RERG +shuffle_97,1,LINC02268 +shuffle_97,2,ATP1A1-AS1 +shuffle_97,3,NEFM +shuffle_97,4,POU2AF1 +shuffle_97,5,ENSG00000233538 +shuffle_97,6,LINC01476 +shuffle_97,7,LINC02015 +shuffle_97,8,POLR2H +shuffle_97,9,PRANCR +shuffle_97,10,PRPF38B +shuffle_97,11,PNCK +shuffle_97,12,WNK4 +shuffle_97,13,TTTY2B +shuffle_97,14,ENSG00000286046 +shuffle_97,15,CENPP +shuffle_97,16,NEGR1 +shuffle_97,17,PCDH15 +shuffle_97,18,IQCA1 +shuffle_98,1,RPH3A +shuffle_98,2,FRMPD4 +shuffle_98,3,SLTM +shuffle_98,4,CADPS +shuffle_98,5,ENSG00000236687 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+shuffle_100,12,DPP3-DT +shuffle_100,13,EPHA5 +shuffle_100,14,LINC00486 +shuffle_100,15,ENSG00000278231 +shuffle_100,16,LINC01307 +shuffle_100,17,LINC02259 +shuffle_100,18,SLC14A2 diff --git a/manuscript/robustness/astrocytes/null_batches/shuffles_96-100/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/astrocytes/null_batches/shuffles_96-100/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..7f6c2b5 --- /dev/null +++ b/manuscript/robustness/astrocytes/null_batches/shuffles_96-100/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,NEAT1,0.9297543695931985,0.9607240468839464,18,threshold,0.00908160654898951,0.00908160654898951,1.0 +shuffle_96,ENSG00000287968,0.4767964044656374,0.5077128540812099,18,max_iterations,0.020465592223074956,0.020465592223074956,0.0 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zUGez_aMtChkV*iN3Qtwm}1`^2}31NA>`q|{@2#?z;KED`}}Dh@S^dn&u$kS<^{!QPb_d}=hW*a zX}~r`=;gb>nnxBW5dLQC^*GQZ>(#jXz(j5V>^Xqe=@y<;o$dnMcf9x-xYm8WXbW)c z=F~GiP@^*e+$RTJ+b*c=2Htb81YC2W0P1%{=j}`du4`O-Z6`2;CIjye2Q7vKjSm6u z!nOx>LxD3c8&9gu23`Fe6;pf>nCz24s(`%#&|KjY;I_(kK3ULqPf*zayecPu((gK8 ziZrbG@!&ZMpLD0!&z!8CCz+?luD|uta#YI5P z{lFUd7_gKAEo#cyR1NaUfn8oGgR#h?a5Nsk1lkU4u+wR|@u*+G!3`uPF#WSHx$tAb Sv?meZLeJCH&t;ucLK6V50JM1k diff --git a/manuscript/robustness/excitatory/alzheimers_coexpression_null_gene_frequency.csv b/manuscript/robustness/excitatory/alzheimers_coexpression_null_gene_frequency.csv index 75cf69a..246cf70 100644 --- a/manuscript/robustness/excitatory/alzheimers_coexpression_null_gene_frequency.csv +++ b/manuscript/robustness/excitatory/alzheimers_coexpression_null_gene_frequency.csv @@ -1,24 +1,24 @@ -gene,rank,n_shuffled_panels,shuffled_frequency,p_value -HSP90AA1,1,0,0.0,0.038461538461538464 -CNTNAP2,2,0,0.0,0.038461538461538464 -SNHG14,3,1,0.04,0.07692307692307693 -MAP1B,4,0,0.0,0.038461538461538464 -LUC7L3,5,0,0.0,0.038461538461538464 -LRFN5,6,0,0.0,0.038461538461538464 -COPG2,7,0,0.0,0.038461538461538464 -TENM2,8,0,0.0,0.038461538461538464 -RBM25,9,0,0.0,0.038461538461538464 -SRSF11,10,0,0.0,0.038461538461538464 -PNISR,11,0,0.0,0.038461538461538464 -AKAP9,12,2,0.08,0.11538461538461539 -LRP1B,13,0,0.0,0.038461538461538464 -DPP10,14,0,0.0,0.038461538461538464 -CDH12,15,0,0.0,0.038461538461538464 -ANKRD26,16,0,0.0,0.038461538461538464 -ANKRD12,17,0,0.0,0.038461538461538464 -PRRC2C,18,1,0.04,0.07692307692307693 -ENSG00000274422,19,0,0.0,0.038461538461538464 -BDP1,20,1,0.04,0.07692307692307693 -TAFA2,21,0,0.0,0.038461538461538464 -EPIC1,22,0,0.0,0.038461538461538464 -ZC3H13,23,0,0.0,0.038461538461538464 +gene,rank,n_shuffled_panels,shuffled_frequency,p_value,p_adj +HSP90AA1,1,0,0.0,0.009900990099009901,0.02277227722772277 +CNTNAP2,2,0,0.0,0.009900990099009901,0.02277227722772277 +SNHG14,3,2,0.02,0.0297029702970297,0.03253182461103253 +MAP1B,4,0,0.0,0.009900990099009901,0.02277227722772277 +LUC7L3,5,1,0.01,0.019801980198019802,0.026790914385556204 +LRFN5,6,2,0.02,0.0297029702970297,0.03253182461103253 +COPG2,7,0,0.0,0.009900990099009901,0.02277227722772277 +TENM2,8,1,0.01,0.019801980198019802,0.026790914385556204 +RBM25,9,0,0.0,0.009900990099009901,0.02277227722772277 +SRSF11,10,4,0.04,0.04950495049504951,0.04950495049504951 +PNISR,11,0,0.0,0.009900990099009901,0.02277227722772277 +AKAP9,12,2,0.02,0.0297029702970297,0.03253182461103253 +LRP1B,13,3,0.03,0.039603960396039604,0.041404140414041404 +DPP10,14,1,0.01,0.019801980198019802,0.026790914385556204 +CDH12,15,0,0.0,0.009900990099009901,0.02277227722772277 +ANKRD26,16,1,0.01,0.019801980198019802,0.026790914385556204 +ANKRD12,17,0,0.0,0.009900990099009901,0.02277227722772277 +PRRC2C,18,1,0.01,0.019801980198019802,0.026790914385556204 +ENSG00000274422,19,1,0.01,0.019801980198019802,0.026790914385556204 +BDP1,20,2,0.02,0.0297029702970297,0.03253182461103253 +TAFA2,21,0,0.0,0.009900990099009901,0.02277227722772277 +EPIC1,22,1,0.01,0.019801980198019802,0.026790914385556204 +ZC3H13,23,0,0.0,0.009900990099009901,0.02277227722772277 diff --git a/manuscript/robustness/excitatory/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/excitatory/alzheimers_coexpression_null_panels.csv index e5c4772..6b72e01 100644 --- a/manuscript/robustness/excitatory/alzheimers_coexpression_null_panels.csv +++ b/manuscript/robustness/excitatory/alzheimers_coexpression_null_panels.csv @@ -597,3 +597,1728 @@ shuffle_25,20,TFAP2C shuffle_25,21,ENSG00000286848 shuffle_25,22,ENSG00000286780 shuffle_25,23,LINC01725 +shuffle_26,1,MED15P7 +shuffle_26,2,LINC02228 +shuffle_26,3,LINC00613 +shuffle_26,4,ENSG00000285663 +shuffle_26,5,NEK2-DT +shuffle_26,6,ENSG00000248172 +shuffle_26,7,ENSG00000286816 +shuffle_26,8,ACSL5 +shuffle_26,9,CUBNP2 +shuffle_26,10,GUSBP14 +shuffle_26,11,ENSG00000244278 +shuffle_26,12,NPHS2 +shuffle_26,13,ENSG00000260112 +shuffle_26,14,HDGFL3P1 +shuffle_26,15,RSL1D1 +shuffle_26,16,CYP4F22 +shuffle_26,17,NDUFA4 +shuffle_26,18,TSNAXIP1 +shuffle_26,19,PDZD2 +shuffle_26,20,TRO +shuffle_26,21,ENSG00000235099 +shuffle_26,22,SLK +shuffle_26,23,ENSG00000254178 +shuffle_27,1,ENSG00000287190 +shuffle_27,2,PAGE2 +shuffle_27,3,NEK2-DT +shuffle_27,4,ENSG00000258394 +shuffle_27,5,LINC02490 +shuffle_27,6,DLX6-AS1 +shuffle_27,7,ENSG00000260112 +shuffle_27,8,PLPP2 +shuffle_27,9,PLPP7 +shuffle_27,10,LENG9 +shuffle_27,11,BMS1P4-AGAP5 +shuffle_27,12,ENSG00000258881 +shuffle_27,13,ENO2 +shuffle_27,14,ENSG00000234261 +shuffle_27,15,ENSG00000286582 +shuffle_27,16,ENSG00000289302 +shuffle_27,17,CCL2 +shuffle_27,18,OR8B10P +shuffle_27,19,SAFB2 +shuffle_27,20,PARM1 +shuffle_27,21,LINC00301 +shuffle_27,22,LINC02824 +shuffle_27,23,ENSG00000237666 +shuffle_28,1,DEXI +shuffle_28,2,ENSG00000229395 +shuffle_28,3,ENSG00000273254 +shuffle_28,4,SCAF11 +shuffle_28,5,ALDH1A1 +shuffle_28,6,ENSG00000289371 +shuffle_28,7,CCDC88A +shuffle_28,8,FAM240A +shuffle_28,9,LINC02240 +shuffle_28,10,CACNG5 +shuffle_28,11,ENSG00000227360 +shuffle_28,12,ENSG00000273244 +shuffle_28,13,MAP9 +shuffle_28,14,LINC02463 +shuffle_28,15,HEPH +shuffle_28,16,ENSG00000235292 +shuffle_28,17,LYSMD1 +shuffle_28,18,ENSG00000259124 +shuffle_28,19,LINC00536 +shuffle_28,20,ENSG00000287204 +shuffle_28,21,ENSG00000256695 +shuffle_28,22,NBEAP6 +shuffle_28,23,ENSG00000260949 +shuffle_29,1,ENSG00000287803 +shuffle_29,2,TUBAL3 +shuffle_29,3,KIF3A +shuffle_29,4,ENSG00000279240 +shuffle_29,5,CUX1 +shuffle_29,6,CASP10 +shuffle_29,7,ENSG00000249951 +shuffle_29,8,HSPA1A +shuffle_29,9,ENSG00000264876 +shuffle_29,10,ENSG00000288796 +shuffle_29,11,ENSG00000262495 +shuffle_29,12,ZBP1 +shuffle_29,13,GUSBP14 +shuffle_29,14,LINC00671 +shuffle_29,15,SPATA20 +shuffle_29,16,ENSG00000230736 +shuffle_29,17,CMTM5 +shuffle_29,18,LINC01726 +shuffle_29,19,VIP +shuffle_29,20,CYP2B6 +shuffle_29,21,ENSG00000278965 +shuffle_29,22,PCSK5 +shuffle_29,23,ENSG00000277128 +shuffle_30,1,STAG1-DT +shuffle_30,2,ZNF597 +shuffle_30,3,ENSG00000253561 +shuffle_30,4,CFAP47 +shuffle_30,5,NOP56P3 +shuffle_30,6,LINC03099 +shuffle_30,7,LINC02323 +shuffle_30,8,ENSG00000287526 +shuffle_30,9,OTUD6A +shuffle_30,10,KRT42P +shuffle_30,11,ADGRL4 +shuffle_30,12,LHFPL3 +shuffle_30,13,TPRX1 +shuffle_30,14,GRTP1 +shuffle_30,15,LINC02228 +shuffle_30,16,ENSG00000278862 +shuffle_30,17,ECHS1 +shuffle_30,18,HMGB1P37 +shuffle_30,19,UTRN +shuffle_30,20,LINC02250 +shuffle_30,21,ENSG00000266923 +shuffle_30,22,LINC03044 +shuffle_30,23,INAVA +shuffle_31,1,TSBP1-AS1 +shuffle_31,2,CCL24 +shuffle_31,3,MKNK2 +shuffle_31,4,C15orf48 +shuffle_31,5,MB +shuffle_31,6,KCNK5 +shuffle_31,7,DGKD +shuffle_31,8,SLC19A4P +shuffle_31,9,ENSG00000215237 +shuffle_31,10,POTEF +shuffle_31,11,LINC02019 +shuffle_31,12,ENSG00000237666 +shuffle_31,13,GLDN +shuffle_31,14,MALRD1 +shuffle_31,15,RAB43 +shuffle_31,16,RPGR +shuffle_31,17,RDH8 +shuffle_31,18,SUSD3 +shuffle_31,19,ENSG00000271519 +shuffle_31,20,ENSG00000273259 +shuffle_31,21,ANKDD1A +shuffle_31,22,ENSG00000286780 +shuffle_31,23,PCDH11X +shuffle_32,1,PCDH17 +shuffle_32,2,RN7SL596P +shuffle_32,3,DDX55 +shuffle_32,4,CFAP210 +shuffle_32,5,SLCO1B1 +shuffle_32,6,CUX2 +shuffle_32,7,STAG3L5P +shuffle_32,8,ZFP42 +shuffle_32,9,PSMC5 +shuffle_32,10,LINC01307 +shuffle_32,11,PTMAP5 +shuffle_32,12,APCDD1 +shuffle_32,13,ENSG00000273476 +shuffle_32,14,IQCA1 +shuffle_32,15,ENSG00000236989 +shuffle_32,16,ENSG00000259395 +shuffle_32,17,GGT5 +shuffle_32,18,TLCD3B +shuffle_32,19,CNTNAP4 +shuffle_32,20,PCAT1 +shuffle_32,21,GNG12 +shuffle_32,22,AMY2A +shuffle_32,23,POM121L2 +shuffle_33,1,ENSG00000289231 +shuffle_33,2,ENSG00000288761 +shuffle_33,3,LINC01958 +shuffle_33,4,SGSM3 +shuffle_33,5,ACVR1C +shuffle_33,6,C1QC +shuffle_33,7,TUBGCP6 +shuffle_33,8,ZAN +shuffle_33,9,ENSG00000280176 +shuffle_33,10,ENSG00000282535 +shuffle_33,11,CTBP1-AS +shuffle_33,12,ENSG00000237844 +shuffle_33,13,MATN3 +shuffle_33,14,SF3B2 +shuffle_33,15,NASP +shuffle_33,16,PSMC5 +shuffle_33,17,IRS4-AS1 +shuffle_33,18,LINC01997 +shuffle_33,19,SPRR2E +shuffle_33,20,POTEF +shuffle_33,21,BAGE2 +shuffle_33,22,LINC01572 +shuffle_33,23,ENSG00000227834 +shuffle_34,1,FCN1 +shuffle_34,2,LINC03078 +shuffle_34,3,POM121L2 +shuffle_34,4,ENSG00000287823 +shuffle_34,5,CHDH +shuffle_34,6,PLCH1 +shuffle_34,7,LRP1B +shuffle_34,8,GAD2 +shuffle_34,9,ENSG00000289105 +shuffle_34,10,LINC02067 +shuffle_34,11,SLC26A9 +shuffle_34,12,MMEL1 +shuffle_34,13,LINC02228 +shuffle_34,14,NDRG2 +shuffle_34,15,ENSG00000287410 +shuffle_34,16,ITK +shuffle_34,17,ATF6-DT +shuffle_34,18,ENSG00000255094 +shuffle_34,19,GPATCH1 +shuffle_34,20,ENSG00000228655 +shuffle_34,21,ENSG00000287147 +shuffle_34,22,DDX21 +shuffle_34,23,ATP8A2P3 +shuffle_35,1,ANK1 +shuffle_35,2,ENSG00000278905 +shuffle_35,3,GLTC1 +shuffle_35,4,PHACTR2 +shuffle_35,5,LNCBRM +shuffle_35,6,ENSG00000289090 +shuffle_35,7,LINC02067 +shuffle_35,8,ENSG00000285669 +shuffle_35,9,ENSG00000289553 +shuffle_35,10,ENSG00000286044 +shuffle_35,11,SOX2-OT +shuffle_35,12,ARFGAP1 +shuffle_35,13,EIF5B +shuffle_35,14,TAFA1 +shuffle_35,15,TCOF1 +shuffle_35,16,ENSG00000264693 +shuffle_35,17,LINC01747 +shuffle_35,18,LINC01781 +shuffle_35,19,ENSG00000279321 +shuffle_35,20,ENSG00000287862 +shuffle_35,21,DUSP10 +shuffle_35,22,LAIR1 +shuffle_35,23,LINC01694 +shuffle_36,1,TRPC6 +shuffle_36,2,PNN +shuffle_36,3,ENSG00000253853 +shuffle_36,4,HIP1 +shuffle_36,5,UROD +shuffle_36,6,SNTG2 +shuffle_36,7,IZUMO3 +shuffle_36,8,LINC02719 +shuffle_36,9,ZNF521 +shuffle_36,10,UBXN4 +shuffle_36,11,RHAG +shuffle_36,12,ARHGAP40 +shuffle_36,13,ENSG00000227527 +shuffle_36,14,POU2AF1 +shuffle_36,15,LRFN5 +shuffle_36,16,TBX21 +shuffle_36,17,DGKK +shuffle_36,18,CASC15 +shuffle_36,19,ENSG00000276570 +shuffle_36,20,ACY1 +shuffle_36,21,ENSG00000234261 +shuffle_36,22,TTTY13 +shuffle_36,23,ENSG00000289302 +shuffle_37,1,SRRT +shuffle_37,2,FGFR3 +shuffle_37,3,LINC02640 +shuffle_37,4,RSL1D1 +shuffle_37,5,TUBGCP6 +shuffle_37,6,RAB33B-AS1 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+shuffle_100,11,ENSG00000259899 +shuffle_100,12,CEP290 +shuffle_100,13,SUSD4 +shuffle_100,14,LINC02130 +shuffle_100,15,Metazoa_SRP-143 +shuffle_100,16,ENSG00000266302 +shuffle_100,17,ENSG00000280225 +shuffle_100,18,ENSG00000234175 +shuffle_100,19,HTRA3 +shuffle_100,20,BOD1L1 +shuffle_100,21,CLK2 +shuffle_100,22,ENSG00000255558 +shuffle_100,23,ENSG00000230803 diff --git a/manuscript/robustness/excitatory/alzheimers_coexpression_null_pvalues.csv b/manuscript/robustness/excitatory/alzheimers_coexpression_null_pvalues.csv index 205560c..a8b98c0 100644 --- a/manuscript/robustness/excitatory/alzheimers_coexpression_null_pvalues.csv +++ b/manuscript/robustness/excitatory/alzheimers_coexpression_null_pvalues.csv @@ -1,3 +1,3 @@ statistic,observed,null_mean,null_min,null_max,n_shuffles,p_value -mean_later_auc,0.9898867121742249,0.5082216892780271,0.5033951644979301,0.5126104681483139,25,0.038461538461538464 -first_auc,0.85228352,0.49970643626666666,0.48836060444444446,0.5136004266666667,25,0.038461538461538464 +mean_later_auc,0.9898867121742247,0.5089230359313066,0.5006614985759519,0.5162488185106726,100,0.009900990099009901 +first_auc,0.85228352,0.5001018951111111,0.4880519111111111,0.5140846222222223,100,0.009900990099009901 diff --git a/manuscript/robustness/excitatory/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/alzheimers_coexpression_null_runs.csv index ce67af0..071703c 100644 --- a/manuscript/robustness/excitatory/alzheimers_coexpression_null_runs.csv +++ b/manuscript/robustness/excitatory/alzheimers_coexpression_null_runs.csv @@ -1,27 +1,102 @@ run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard -observed,HSP90AA1,0.85228352,0.9898867121742249,23,patience,0.0106,0.01308,1.0 -shuffle_1,CCL24,0.49734101333333336,0.506231232117245,23,max_iterations,0.03028,0.03764,0.0 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+shuffle_50,5,ENSG00000254350 +shuffle_50,6,ENSG00000286756 +shuffle_50,7,ENSG00000237445 +shuffle_50,8,ENSG00000203262 +shuffle_50,9,EXOC1L +shuffle_50,10,RABL6 +shuffle_50,11,CUX2 +shuffle_50,12,Metazoa_SRP-143 +shuffle_50,13,ENSG00000271860 +shuffle_50,14,SRRM1 +shuffle_50,15,CD300E +shuffle_50,16,HNF4G +shuffle_50,17,ENSG00000286221 +shuffle_50,18,CUX1 +shuffle_50,19,ENSG00000260364 +shuffle_50,20,SMC1A +shuffle_50,21,EPPK1 +shuffle_50,22,EFS +shuffle_50,23,OR7A17 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_26-50/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_26-50/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..291c2fa --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_26-50/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,27 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898867121742249,23,patience,0.0106,0.01308,1.0 +shuffle_26,MED15P7,0.5071645866666666,0.5103948399074577,23,max_iterations,0.0244,0.03792,0.0 +shuffle_27,ENSG00000287190,0.49986872888888884,0.505923607312932,23,max_iterations,0.02912,0.0398,0.0 +shuffle_28,DEXI,0.49615224888888887,0.5039365970062668,23,max_iterations,0.0272,0.08524,0.0 +shuffle_29,ENSG00000287803,0.5090731377777777,0.5082139173549621,23,max_iterations,0.02804,0.02804,0.0 +shuffle_30,STAG1-DT,0.4964974222222222,0.5152071031885525,23,max_iterations,0.032,0.03664,0.0 +shuffle_31,TSBP1-AS1,0.5007662222222222,0.5126775435421973,23,max_iterations,0.02816,0.19148,0.0 +shuffle_32,PCDH17,0.49810460444444443,0.5127791831233078,23,max_iterations,0.02804,0.08736,0.0 +shuffle_33,ENSG00000289231,0.4948384,0.5087854459942659,23,max_iterations,0.02828,0.03628,0.0 +shuffle_34,FCN1,0.5037469866666666,0.5114879191200672,23,max_iterations,0.03024,0.03452,0.022222222222222223 +shuffle_35,ANK1,0.50001792,0.5118961666723627,23,max_iterations,0.03132,0.08948,0.0 +shuffle_36,TRPC6,0.48932913777777776,0.5108852033350391,23,max_iterations,0.02976,0.037,0.022222222222222223 +shuffle_37,SRRT,0.5003759644444444,0.5105971428935764,23,max_iterations,0.02992,0.08652,0.0 +shuffle_38,TRAPPC3L,0.49399786666666673,0.5120324075421495,23,max_iterations,0.02388,0.04848,0.045454545454545456 +shuffle_39,MIA3,0.4986519466666666,0.509728018476242,23,max_iterations,0.02848,0.07804,0.0 +shuffle_40,LINC02417,0.49458140444444443,0.5126449894448132,23,max_iterations,0.0284,0.08664,0.0 +shuffle_41,FMR1NB,0.5014117688888888,0.5122878816477523,23,max_iterations,0.02948,0.07568,0.0 +shuffle_42,OLAH,0.4945230222222222,0.5091936846079309,23,max_iterations,0.03224,0.07088,0.0 +shuffle_43,TRIM40,0.5053233066666667,0.5036170128082272,23,max_iterations,0.03004,0.03796,0.0 +shuffle_44,PITPNC1,0.5068449422222222,0.5132071976098701,23,max_iterations,0.03,0.03628,0.022222222222222223 +shuffle_45,TRBV23-1,0.5074665244444445,0.508050787481704,23,max_iterations,0.03032,0.03712,0.0 +shuffle_46,ENSG00000226816,0.49241450666666664,0.5075262853002477,23,max_iterations,0.02772,0.03972,0.0 +shuffle_47,IYD,0.4989587200000001,0.509890791403119,23,max_iterations,0.02776,0.04348,0.022222222222222223 +shuffle_48,HK2-DT,0.49643185777777776,0.5057861092014923,23,max_iterations,0.02872,0.0324,0.022222222222222223 +shuffle_49,RUFY4,0.49673073777777776,0.5137269920327641,23,max_iterations,0.02688,0.07488,0.0 +shuffle_50,PLIN5,0.4995232,0.511891180310355,23,max_iterations,0.0278,0.06864,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_51-55/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_51-55/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..ede94fa --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_51-55/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 +observed,19,0.01312 +observed,20,0.01424 +observed,21,0.01368 +observed,22,0.0138 +observed,23,0.01308 +shuffle_51,2,0.06576 +shuffle_51,3,0.06472 +shuffle_51,4,0.05976 +shuffle_51,5,0.05552 +shuffle_51,6,0.05396 +shuffle_51,7,0.0472 +shuffle_51,8,0.0396 +shuffle_51,9,0.04264 +shuffle_51,10,0.0386 +shuffle_51,11,0.03632 +shuffle_51,12,0.031 +shuffle_51,13,0.02968 +shuffle_51,14,0.02836 +shuffle_51,15,0.2448 +shuffle_51,16,0.05708 +shuffle_51,17,0.14168 +shuffle_51,18,0.04224 +shuffle_51,19,0.08036 +shuffle_51,20,0.09164 +shuffle_51,21,0.0838 +shuffle_51,22,0.07968 +shuffle_52,2,0.07056 +shuffle_52,3,0.06348 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b/manuscript/robustness/excitatory/null_batches/shuffles_51-55/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..b3870f9 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_51-55/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 +observed,17,ANKRD12 +observed,18,PRRC2C +observed,19,ENSG00000274422 +observed,20,BDP1 +observed,21,TAFA2 +observed,22,EPIC1 +observed,23,ZC3H13 +shuffle_51,1,FLACC1 +shuffle_51,2,CCDC144BP +shuffle_51,3,KCNH7 +shuffle_51,4,ENSG00000285201 +shuffle_51,5,DTL +shuffle_51,6,FFAR2 +shuffle_51,7,ENSG00000285663 +shuffle_51,8,GLYATL3 +shuffle_51,9,RERG +shuffle_51,10,HLA-DOA +shuffle_51,11,ACKR3 +shuffle_51,12,LINC02485 +shuffle_51,13,ENSG00000288023 +shuffle_51,14,CASC17 +shuffle_51,15,LINC02964 +shuffle_51,16,ENSG00000248172 +shuffle_51,17,ENSG00000278766 +shuffle_51,18,RNU6-1228P +shuffle_51,19,UNC13C +shuffle_51,20,KIF19 +shuffle_51,21,ENSG00000255558 +shuffle_51,22,NRSN2-AS1 +shuffle_51,23,CAPN3 +shuffle_52,1,SRRM1 +shuffle_52,2,CCDC33 +shuffle_52,3,GNE +shuffle_52,4,ENSG00000287231 +shuffle_52,5,LINC03057 +shuffle_52,6,ENSG00000225970 +shuffle_52,7,CARD6 +shuffle_52,8,MIR4290HG +shuffle_52,9,H1-4 +shuffle_52,10,BMPR1B-DT +shuffle_52,11,SGCZ +shuffle_52,12,GPC5 +shuffle_52,13,SELENOV +shuffle_52,14,ENSG00000249642 +shuffle_52,15,ENSG00000266283 +shuffle_52,16,KNDC1 +shuffle_52,17,ANKRD24 +shuffle_52,18,LINC02999 +shuffle_52,19,ENSG00000226816 +shuffle_52,20,CALHM3 +shuffle_52,21,ENSG00000261578 +shuffle_52,22,ENSG00000248317 +shuffle_52,23,LINC02640 +shuffle_53,1,GTF3C2-AS1 +shuffle_53,2,CX3CR1 +shuffle_53,3,SNTG2 +shuffle_53,4,DES +shuffle_53,5,ENSG00000234378 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+run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_51,FLACC1,0.49284657777777774,0.5049238942822635,23,max_iterations,0.02836,0.07968,0.0 +shuffle_52,SRRM1,0.5084677688888889,0.5036209714809217,23,max_iterations,0.03072,0.03468,0.0 +shuffle_53,GTF3C2-AS1,0.5119008711111112,0.5098067176442409,23,max_iterations,0.02776,0.04916,0.0 +shuffle_54,TULP2,0.4955120355555555,0.5042576853337558,23,max_iterations,0.02808,0.03632,0.0 +shuffle_55,EPHA6,0.5024445866666667,0.5059315837035917,23,max_iterations,0.02984,0.0648,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..4289a69 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 +observed,19,0.01312 +observed,20,0.01424 +observed,21,0.01368 +observed,22,0.0138 +observed,23,0.01308 +shuffle_56,2,0.07048 +shuffle_56,3,0.06492 +shuffle_56,4,0.06184 +shuffle_56,5,0.05652 +shuffle_56,6,0.05168 +shuffle_56,7,0.04548 +shuffle_56,8,0.04128 +shuffle_56,9,0.03632 +shuffle_56,10,0.03988 +shuffle_56,11,0.03372 +shuffle_56,12,0.02952 +shuffle_56,13,0.02868 +shuffle_56,14,0.27828 +shuffle_56,15,0.08204 +shuffle_56,16,0.06488 +shuffle_56,17,0.05868 +shuffle_56,18,0.10224 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a/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..c174e81 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 +observed,17,ANKRD12 +observed,18,PRRC2C +observed,19,ENSG00000274422 +observed,20,BDP1 +observed,21,TAFA2 +observed,22,EPIC1 +observed,23,ZC3H13 +shuffle_56,1,ENSG00000234261 +shuffle_56,2,ENSG00000289079 +shuffle_56,3,LINC02490 +shuffle_56,4,CLK2 +shuffle_56,5,APCDD1 +shuffle_56,6,SLC8A1-AS1 +shuffle_56,7,CASKP1 +shuffle_56,8,PLA2G4F +shuffle_56,9,CCDC40 +shuffle_56,10,TECTB +shuffle_56,11,LINC01517 +shuffle_56,12,CENPM +shuffle_56,13,LINC02485 +shuffle_56,14,Metazoa_SRP-143 +shuffle_56,15,ENSG00000287862 +shuffle_56,16,DNAJC22 +shuffle_56,17,LINC00276 +shuffle_56,18,ENSG00000276418 +shuffle_56,19,C1orf141 +shuffle_56,20,NTSR2 +shuffle_56,21,ENSG00000288016 +shuffle_56,22,LINC02243 +shuffle_56,23,ENSG00000259124 +shuffle_57,1,BMPR1B-DT +shuffle_57,2,ENSG00000235274 +shuffle_57,3,ENSG00000236431 +shuffle_57,4,ENSG00000280176 +shuffle_57,5,NUP133-DT +shuffle_57,6,LINC02183 +shuffle_57,7,ENSG00000241884 +shuffle_57,8,GPC5 +shuffle_57,9,NTNG1 +shuffle_57,10,ENSG00000261578 +shuffle_57,11,HSH2D +shuffle_57,12,MCHR2 +shuffle_57,13,KLHL4 +shuffle_57,14,MID1 +shuffle_57,15,APOBEC1 +shuffle_57,16,ENSG00000287001 +shuffle_57,17,ENSG00000286765 +shuffle_57,18,FSCB +shuffle_57,19,DPPA3 +shuffle_57,20,LAX1 +shuffle_57,21,ENSG00000249881 +shuffle_57,22,ENSG00000273259 +shuffle_57,23,DNASE1L3 +shuffle_58,1,NEMF +shuffle_58,2,ENSG00000259124 +shuffle_58,3,BLVRBP1 +shuffle_58,4,ENSG00000287079 +shuffle_58,5,NDUFA6-DT +shuffle_58,6,GTF2IRD2P1 +shuffle_58,7,CENPC +shuffle_58,8,GLB1L2 +shuffle_58,9,STAC +shuffle_58,10,KIR3DX1 +shuffle_58,11,NUTM1 +shuffle_58,12,ANKRD11P1 +shuffle_58,13,ENSG00000237445 +shuffle_58,14,LINC02142 +shuffle_58,15,MID1 +shuffle_58,16,LNCNEF +shuffle_58,17,RGS12 +shuffle_58,18,ENSG00000266283 +shuffle_58,19,TYROBP +shuffle_58,20,LINC02111 +shuffle_58,21,ENSG00000286514 +shuffle_58,22,MTHFD2P1 +shuffle_58,23,LINC01081 +shuffle_59,1,LINC01467 +shuffle_59,2,ENSG00000226816 +shuffle_59,3,LINC01075 +shuffle_59,4,PSMA7 +shuffle_59,5,GSEC +shuffle_59,6,MRPL40 +shuffle_59,7,IK +shuffle_59,8,FBN3 +shuffle_59,9,ENSG00000279428 +shuffle_59,10,EFCAB9 +shuffle_59,11,BMP7 +shuffle_59,12,ENSG00000258312 +shuffle_59,13,LINC02643 +shuffle_59,14,ENSG00000254847 +shuffle_59,15,ENSG00000285563 +shuffle_59,16,C18orf63 +shuffle_59,17,KIF19 +shuffle_59,18,TOX +shuffle_59,19,LINC01740 +shuffle_59,20,RARRES2 +shuffle_59,21,PGM5P4 +shuffle_59,22,ENSG00000227579 +shuffle_59,23,LINC02795 +shuffle_60,1,ENSG00000258346 +shuffle_60,2,PSMD3 +shuffle_60,3,ENSG00000253879 +shuffle_60,4,ENSG00000279708 +shuffle_60,5,RNU6-1228P +shuffle_60,6,LINC03025 +shuffle_60,7,PSMA7 +shuffle_60,8,CASKP1 +shuffle_60,9,ANKRD62 +shuffle_60,10,LINC01965 +shuffle_60,11,LHFPL3 +shuffle_60,12,ECHDC2 +shuffle_60,13,RNF103-CHMP3 +shuffle_60,14,TSBP1-AS1 +shuffle_60,15,ENSG00000253295 +shuffle_60,16,SBF1P2 +shuffle_60,17,LINC00885 +shuffle_60,18,ENSG00000173357 +shuffle_60,19,ENSG00000279936 +shuffle_60,20,TOX +shuffle_60,21,HIP1 +shuffle_60,22,GSTA2 +shuffle_60,23,ATF6-DT diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..4bc8f94 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_56-60/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_56,ENSG00000234261,0.5005404444444445,0.5105783874277979,23,max_iterations,0.02868,0.08188,0.0 +shuffle_57,BMPR1B-DT,0.49369344000000004,0.5058443673211817,23,max_iterations,0.031,0.07664,0.0 +shuffle_58,NEMF,0.5118735644444444,0.506733078634009,23,max_iterations,0.02444,0.0406,0.0 +shuffle_59,LINC01467,0.5043315911111111,0.5058688232231585,23,max_iterations,0.02908,0.08312,0.0 +shuffle_60,ENSG00000258346,0.5035554133333333,0.512879646599984,23,max_iterations,0.03112,0.07476,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..05b3d84 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 +observed,19,0.01312 +observed,20,0.01424 +observed,21,0.01368 +observed,22,0.0138 +observed,23,0.01308 +shuffle_61,2,0.06912 +shuffle_61,3,0.06464 +shuffle_61,4,0.0598 +shuffle_61,5,0.056 +shuffle_61,6,0.05084 +shuffle_61,7,0.0476 +shuffle_61,8,0.04428 +shuffle_61,9,0.04368 +shuffle_61,10,0.03884 +shuffle_61,11,0.03632 +shuffle_61,12,0.03212 +shuffle_61,13,0.02568 +shuffle_61,14,0.02412 +shuffle_61,15,0.30744 +shuffle_61,16,0.04376 +shuffle_61,17,0.11048 +shuffle_61,18,0.06312 +shuffle_61,19,0.04032 +shuffle_61,20,0.08444 +shuffle_61,21,0.0782 +shuffle_61,22,0.04192 +shuffle_62,2,0.07068 +shuffle_62,3,0.06592 +shuffle_62,4,0.05956 +shuffle_62,5,0.05752 +shuffle_62,6,0.05052 +shuffle_62,7,0.04792 +shuffle_62,8,0.04116 +shuffle_62,9,0.0406 +shuffle_62,10,0.0354 +shuffle_62,11,0.03516 +shuffle_62,12,0.03268 +shuffle_62,13,0.0306 +shuffle_62,14,0.27844 +shuffle_62,15,0.03728 +shuffle_62,16,0.12528 +shuffle_62,17,0.05396 +shuffle_62,18,0.03644 +shuffle_62,19,0.03464 +shuffle_62,20,0.03416 +shuffle_62,21,0.03388 +shuffle_62,22,0.03244 +shuffle_63,2,0.07156 +shuffle_63,3,0.06388 +shuffle_63,4,0.0562 +shuffle_63,5,0.05636 +shuffle_63,6,0.05368 +shuffle_63,7,0.05048 +shuffle_63,8,0.04536 +shuffle_63,9,0.0422 +shuffle_63,10,0.03856 +shuffle_63,11,0.03392 +shuffle_63,12,0.03276 +shuffle_63,13,0.02912 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+shuffle_65,16,0.06856 +shuffle_65,17,0.08296 +shuffle_65,18,0.04428 +shuffle_65,19,0.09124 +shuffle_65,20,0.0304 +shuffle_65,21,0.06124 +shuffle_65,22,0.04684 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..915fdfe --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 +observed,17,ANKRD12 +observed,18,PRRC2C +observed,19,ENSG00000274422 +observed,20,BDP1 +observed,21,TAFA2 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a/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..4a81a07 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_61-65/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_61,TRPC6,0.5040477866666667,0.5089659872873664,23,max_iterations,0.02412,0.04192,0.0 +shuffle_62,ENSG00000251031,0.5086047288888889,0.5037049161061861,23,max_iterations,0.0306,0.03244,0.0 +shuffle_63,C1orf127,0.49625408,0.5087075554352435,23,max_iterations,0.02672,0.05028,0.0 +shuffle_64,SALL3,0.5023908977777778,0.5154928570731989,23,max_iterations,0.02756,0.09472,0.022222222222222223 +shuffle_65,IK,0.4970710755555555,0.5082208679227015,23,max_iterations,0.02544,0.04684,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..afd0e12 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 +observed,19,0.01312 +observed,20,0.01424 +observed,21,0.01368 +observed,22,0.0138 +observed,23,0.01308 +shuffle_66,2,0.06976 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+shuffle_70,7,0.04744 +shuffle_70,8,0.0432 +shuffle_70,9,0.03988 +shuffle_70,10,0.03968 +shuffle_70,11,0.03332 +shuffle_70,12,0.0322 +shuffle_70,13,0.0328 +shuffle_70,14,0.24128 +shuffle_70,15,0.14928 +shuffle_70,16,0.11916 +shuffle_70,17,0.12308 +shuffle_70,18,0.11708 +shuffle_70,19,0.07848 +shuffle_70,20,0.08424 +shuffle_70,21,0.02868 +shuffle_70,22,0.03388 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..ea314d8 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR 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+shuffle_70,21,PRECSIT +shuffle_70,22,ENSG00000250646 +shuffle_70,23,HS3ST4 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..311d728 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_66-70/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_66,ENSG00000288013,0.5140846222222223,0.5068587545940054,23,max_iterations,0.02432,0.07244,0.022222222222222223 +shuffle_67,ENSG00000235726,0.4956753066666667,0.5107498168724933,23,max_iterations,0.02932,0.07628,0.022222222222222223 +shuffle_68,ADGRE4P,0.49697344000000004,0.5053469026007223,23,max_iterations,0.02984,0.07348,0.0 +shuffle_69,LINC01572,0.5043555555555556,0.5052001731365535,23,max_iterations,0.02948,0.0332,0.0 +shuffle_70,OR13J1,0.4966107022222222,0.5080616365950906,23,max_iterations,0.02868,0.03388,0.022222222222222223 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..b4b5e8f --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 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+shuffle_75,14,AADACL3 +shuffle_75,15,ENSG00000233242 +shuffle_75,16,TTTY13 +shuffle_75,17,RHAG +shuffle_75,18,XPNPEP2 +shuffle_75,19,TFAP2C +shuffle_75,20,ECHS1 +shuffle_75,21,CCDC144CP +shuffle_75,22,PPME1 +shuffle_75,23,CASP10 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_71-75/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_71-75/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..9bcdab7 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_71-75/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_71,RUFY4,0.4963524977777778,0.5088954658825652,23,max_iterations,0.0268,0.03596,0.0 +shuffle_72,ENSG00000279960,0.5102444088888889,0.5150160224904266,23,max_iterations,0.03116,0.0804,0.0 +shuffle_73,ITPR2,0.5077110044444445,0.5069784115804201,23,max_iterations,0.02624,0.03316,0.022222222222222223 +shuffle_74,ENSG00000251168,0.4965650488888889,0.512138387667611,23,max_iterations,0.02948,0.04672,0.0 +shuffle_75,ENSG00000254347,0.49640220444444444,0.5148415162307312,23,max_iterations,0.02744,0.04156,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_76-80/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_76-80/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..db3e265 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_76-80/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 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+observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 +observed,17,ANKRD12 +observed,18,PRRC2C +observed,19,ENSG00000274422 +observed,20,BDP1 +observed,21,TAFA2 +observed,22,EPIC1 +observed,23,ZC3H13 +shuffle_76,1,ENSG00000249881 +shuffle_76,2,ENSG00000267282 +shuffle_76,3,RN7SL556P +shuffle_76,4,GALNTL6 +shuffle_76,5,ENSG00000234378 +shuffle_76,6,LINC02111 +shuffle_76,7,SFRP1 +shuffle_76,8,PCDH11X +shuffle_76,9,CYP17A1 +shuffle_76,10,UPP1 +shuffle_76,11,PCDH11Y +shuffle_76,12,GSTCD +shuffle_76,13,ENSG00000254187 +shuffle_76,14,TM4SF4 +shuffle_76,15,TNFAIP8 +shuffle_76,16,SNTG2 +shuffle_76,17,TMEM252-DT +shuffle_76,18,ENSG00000287352 +shuffle_76,19,POT1-AS1 +shuffle_76,20,SOX2-OT +shuffle_76,21,LINC00550 +shuffle_76,22,ENSG00000289090 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+shuffle_80,12,CYP2C9 +shuffle_80,13,PON3 +shuffle_80,14,CFAP210 +shuffle_80,15,LINC01982 +shuffle_80,16,SREK1IP1P1 +shuffle_80,17,ENSG00000256695 +shuffle_80,18,ENSG00000286743 +shuffle_80,19,SHISA6 +shuffle_80,20,HTRA3 +shuffle_80,21,ENSG00000275119 +shuffle_80,22,TSBP1-AS1 +shuffle_80,23,ENSG00000237480 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..b4f9461 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_76,ENSG00000249881,0.5028612266666667,0.5113308882789058,23,max_iterations,0.02832,0.04224,0.0 +shuffle_77,LINC01527,0.49521436444444444,0.5100721444538849,23,max_iterations,0.0246,0.07444,0.0 +shuffle_78,NXPH1,0.4963083377777778,0.5089635924406095,23,max_iterations,0.02784,0.04204,0.022222222222222223 +shuffle_79,ENSG00000244113,0.5034077866666666,0.5083285992430554,23,max_iterations,0.02988,0.07228,0.0 +shuffle_80,ENSG00000255347,0.5044677688888889,0.5073029324831085,23,max_iterations,0.03104,0.03684,0.022222222222222223 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_81-85/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_81-85/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..71baec4 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_81-85/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 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b/manuscript/robustness/excitatory/null_batches/shuffles_81-85/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 +observed,17,ANKRD12 +observed,18,PRRC2C +observed,19,ENSG00000274422 +observed,20,BDP1 +observed,21,TAFA2 +observed,22,EPIC1 +observed,23,ZC3H13 +shuffle_81,1,ENSG00000249072 +shuffle_81,2,SMIM31 +shuffle_81,3,CFAP61 +shuffle_81,4,HSP90B1 +shuffle_81,5,SYS1 +shuffle_81,6,CFAP299 +shuffle_81,7,ENSG00000288811 +shuffle_81,8,SRSF11 +shuffle_81,9,ENSG00000243969 +shuffle_81,10,CDH20 +shuffle_81,11,LINC01378 +shuffle_81,12,PROP1 +shuffle_81,13,WWP1P1 +shuffle_81,14,ENSG00000226816 +shuffle_81,15,LRMDA +shuffle_81,16,HSPD1P6 +shuffle_81,17,EPHA6 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+shuffle_85,6,LINC01877 +shuffle_85,7,ENSG00000237844 +shuffle_85,8,SLC18A1 +shuffle_85,9,CIDEC +shuffle_85,10,ENSG00000229896 +shuffle_85,11,LYSMD1 +shuffle_85,12,LINC01746 +shuffle_85,13,HTRA3 +shuffle_85,14,FBN3 +shuffle_85,15,PSMC5 +shuffle_85,16,ENSG00000240687 +shuffle_85,17,CENPC +shuffle_85,18,HSP90AB2P +shuffle_85,19,HMGN5 +shuffle_85,20,NAP1L1P1 +shuffle_85,21,SLK +shuffle_85,22,ENSG00000250899 +shuffle_85,23,RILPL2 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_81-85/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_81-85/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..a504ddc --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_81-85/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_81,ENSG00000249072,0.4984524088888889,0.5140015304987535,23,max_iterations,0.02648,0.19488,0.022222222222222223 +shuffle_82,SLC26A9,0.4880519111111111,0.5095053256317789,23,max_iterations,0.0284,0.03548,0.022222222222222223 +shuffle_83,TTC24,0.5029237333333334,0.5090854548993969,23,max_iterations,0.032,0.09228,0.0 +shuffle_84,LINC00967,0.5105165511111112,0.5038817408555001,23,max_iterations,0.02972,0.03116,0.0 +shuffle_85,ENSG00000283602,0.4959265422222222,0.5160727509128995,23,max_iterations,0.03068,0.24452,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..26703f2 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 +observed,19,0.01312 +observed,20,0.01424 +observed,21,0.01368 +observed,22,0.0138 +observed,23,0.01308 +shuffle_86,2,0.0714 +shuffle_86,3,0.06644 +shuffle_86,4,0.05812 +shuffle_86,5,0.05488 +shuffle_86,6,0.05164 +shuffle_86,7,0.04764 +shuffle_86,8,0.04624 +shuffle_86,9,0.04144 +shuffle_86,10,0.0348 +shuffle_86,11,0.03008 +shuffle_86,12,0.03028 +shuffle_86,13,0.031 +shuffle_86,14,0.27176 +shuffle_86,15,0.09512 +shuffle_86,16,0.08704 +shuffle_86,17,0.05464 +shuffle_86,18,0.05044 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a/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..c360205 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 +observed,17,ANKRD12 +observed,18,PRRC2C +observed,19,ENSG00000274422 +observed,20,BDP1 +observed,21,TAFA2 +observed,22,EPIC1 +observed,23,ZC3H13 +shuffle_86,1,VN1R7P +shuffle_86,2,C15orf48 +shuffle_86,3,SUSD4 +shuffle_86,4,PYHIN1 +shuffle_86,5,TBXA2R +shuffle_86,6,ENSG00000285894 +shuffle_86,7,RERG +shuffle_86,8,BMP7 +shuffle_86,9,ZNF728 +shuffle_86,10,STAG1-DT +shuffle_86,11,LINC02130 +shuffle_86,12,LINC02138 +shuffle_86,13,ACADVL +shuffle_86,14,KCNG2 +shuffle_86,15,C8orf88 +shuffle_86,16,FOXE1 +shuffle_86,17,IFNL3 +shuffle_86,18,CCDC181 +shuffle_86,19,LINC02481 +shuffle_86,20,ATMIN +shuffle_86,21,UHRF1 +shuffle_86,22,ENSG00000289570 +shuffle_86,23,LGALS2 +shuffle_87,1,SCNN1G +shuffle_87,2,ENSG00000287147 +shuffle_87,3,ENSG00000276693 +shuffle_87,4,IFNL3 +shuffle_87,5,SLC9B1P2 +shuffle_87,6,MED15P7 +shuffle_87,7,TTTY22 +shuffle_87,8,CDH13 +shuffle_87,9,ENSG00000262488 +shuffle_87,10,ENSG00000280302 +shuffle_87,11,PROS2P +shuffle_87,12,ENSG00000254261 +shuffle_87,13,ENSG00000280441 +shuffle_87,14,ADAMTSL1 +shuffle_87,15,MYL10 +shuffle_87,16,ENSG00000250149 +shuffle_87,17,RHOT2 +shuffle_87,18,SYCP2 +shuffle_87,19,RSPH6A +shuffle_87,20,ENSG00000249590 +shuffle_87,21,C18orf63 +shuffle_87,22,ENSG00000266283 +shuffle_87,23,ZFHX4-AS1 +shuffle_88,1,ENSG00000287231 +shuffle_88,2,SLC17A8 +shuffle_88,3,SMIM31 +shuffle_88,4,PRIMA1 +shuffle_88,5,ENSG00000288811 +shuffle_88,6,ENSG00000289570 +shuffle_88,7,CD300E +shuffle_88,8,PLA2G4F +shuffle_88,9,HAS2-AS1 +shuffle_88,10,SOAT2 +shuffle_88,11,MCHR2 +shuffle_88,12,LENG9 +shuffle_88,13,LUZP2 +shuffle_88,14,ENSG00000279240 +shuffle_88,15,ADAMTS17 +shuffle_88,16,ENSG00000253177 +shuffle_88,17,CDX4 +shuffle_88,18,ENSG00000285563 +shuffle_88,19,FLACC1 +shuffle_88,20,BTBD17 +shuffle_88,21,LINC01656 +shuffle_88,22,SUSD4 +shuffle_88,23,ENSG00000259395 +shuffle_89,1,LINC02453 +shuffle_89,2,FGR +shuffle_89,3,ENSG00000264876 +shuffle_89,4,PGM5P4 +shuffle_89,5,ENSG00000236710 +shuffle_89,6,CENPM +shuffle_89,7,ENSG00000285586 +shuffle_89,8,SLC39A5 +shuffle_89,9,GGT5 +shuffle_89,10,ANKRD18B +shuffle_89,11,RBMY1A3P +shuffle_89,12,PVALB +shuffle_89,13,ENSG00000241884 +shuffle_89,14,RALBP1 +shuffle_89,15,ENSG00000285663 +shuffle_89,16,PARD3B +shuffle_89,17,LINC01877 +shuffle_89,18,DHX35-DT +shuffle_89,19,GRIK1 +shuffle_89,20,SOHLH1 +shuffle_89,21,UMLILO +shuffle_89,22,MISP +shuffle_89,23,FOXB1 +shuffle_90,1,LINC02240 +shuffle_90,2,TRIM50 +shuffle_90,3,ENSG00000226816 +shuffle_90,4,POTEG +shuffle_90,5,ENSG00000254092 +shuffle_90,6,ANKRD26P3 +shuffle_90,7,PABPC3 +shuffle_90,8,LYZ +shuffle_90,9,CKMT1A +shuffle_90,10,YWHAH +shuffle_90,11,ENSG00000286474 +shuffle_90,12,GSTA10P +shuffle_90,13,LINC01609 +shuffle_90,14,ENSG00000279650 +shuffle_90,15,ENSG00000224905 +shuffle_90,16,NASP +shuffle_90,17,ENSG00000278766 +shuffle_90,18,LINC01904 +shuffle_90,19,HTR1F +shuffle_90,20,LINC01427 +shuffle_90,21,PGLYRP2 +shuffle_90,22,ATP5ME +shuffle_90,23,RABL6 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..356b5cb --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_86,VN1R7P,0.4994222933333333,0.5075360818665975,23,max_iterations,0.03008,0.04428,0.0 +shuffle_87,SCNN1G,0.5016144355555556,0.5006614985759519,23,max_iterations,0.02824,0.03436,0.0 +shuffle_88,ENSG00000287231,0.4922326755555556,0.5045538476739565,23,max_iterations,0.02876,0.08788,0.0 +shuffle_89,LINC02453,0.49393415111111116,0.5057127461788258,23,max_iterations,0.02956,0.03688,0.0 +shuffle_90,LINC02240,0.4967886933333333,0.506190118294034,23,max_iterations,0.02488,0.03148,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..4b9914d --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 +observed,19,0.01312 +observed,20,0.01424 +observed,21,0.01368 +observed,22,0.0138 +observed,23,0.01308 +shuffle_91,2,0.06764 +shuffle_91,3,0.06344 +shuffle_91,4,0.06308 +shuffle_91,5,0.05552 +shuffle_91,6,0.04952 +shuffle_91,7,0.04972 +shuffle_91,8,0.04264 +shuffle_91,9,0.03952 +shuffle_91,10,0.03772 +shuffle_91,11,0.03596 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+shuffle_95,16,0.06644 +shuffle_95,17,0.06508 +shuffle_95,18,0.0576 +shuffle_95,19,0.04452 +shuffle_95,20,0.04472 +shuffle_95,21,0.07808 +shuffle_95,22,0.15824 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..bc3adcc --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 +observed,17,ANKRD12 +observed,18,PRRC2C +observed,19,ENSG00000274422 +observed,20,BDP1 +observed,21,TAFA2 +observed,22,EPIC1 +observed,23,ZC3H13 +shuffle_91,1,IGSF23 +shuffle_91,2,LINC02397 +shuffle_91,3,MTMR8 +shuffle_91,4,TMEM78 +shuffle_91,5,C1QC +shuffle_91,6,LINC02252 +shuffle_91,7,TULP2 +shuffle_91,8,DEFA5 +shuffle_91,9,ENSG00000283431 +shuffle_91,10,ENSG00000288102 +shuffle_91,11,CENPM +shuffle_91,12,BCAN-AS1 +shuffle_91,13,ENSG00000289492 +shuffle_91,14,CASC15 +shuffle_91,15,LNCBRM +shuffle_91,16,ALCAM +shuffle_91,17,BAZ1B +shuffle_91,18,IL1RAPL2 +shuffle_91,19,LINC00301 +shuffle_91,20,ENSG00000255094 +shuffle_91,21,ENSG00000251168 +shuffle_91,22,TMEM231 +shuffle_91,23,RNU6-147P +shuffle_92,1,RBM5-AS1 +shuffle_92,2,MIATNB +shuffle_92,3,RFX5-AS1 +shuffle_92,4,SLC8A1-AS1 +shuffle_92,5,Y_RNA-711 +shuffle_92,6,KMT2A +shuffle_92,7,C11orf21 +shuffle_92,8,ADAD1 +shuffle_92,9,PCP4 +shuffle_92,10,RNF103-CHMP3 +shuffle_92,11,CASP4LP +shuffle_92,12,TCOF1 +shuffle_92,13,ENSG00000253659 +shuffle_92,14,APBB1 +shuffle_92,15,HACD4 +shuffle_92,16,POTEA +shuffle_92,17,ENSG00000248605 +shuffle_92,18,SDK1-AS1 +shuffle_92,19,ENSG00000253177 +shuffle_92,20,FCAR +shuffle_92,21,RAB43 +shuffle_92,22,ENSG00000287092 +shuffle_92,23,ENSG00000253583 +shuffle_93,1,NCOA4 +shuffle_93,2,MPIG6B +shuffle_93,3,LINC02891 +shuffle_93,4,MIR4300HG +shuffle_93,5,ENSG00000287861 +shuffle_93,6,CASC15 +shuffle_93,7,ENSG00000254347 +shuffle_93,8,ENSG00000235292 +shuffle_93,9,TPT1 +shuffle_93,10,VIP +shuffle_93,11,HAPLN1 +shuffle_93,12,ENSG00000261312 +shuffle_93,13,ZC3H11B +shuffle_93,14,ENSG00000259624 +shuffle_93,15,ENSG00000287912 +shuffle_93,16,FRMD4B +shuffle_93,17,ENSG00000289371 +shuffle_93,18,SLC1A3 +shuffle_93,19,LINC02243 +shuffle_93,20,PAPLN +shuffle_93,21,WIPF3 +shuffle_93,22,HLA-F-AS1 +shuffle_93,23,SLC22A16 +shuffle_94,1,PRCD +shuffle_94,2,EDA2R +shuffle_94,3,GSTA11P +shuffle_94,4,CD8B2 +shuffle_94,5,ENSG00000283602 +shuffle_94,6,ENSG00000267666 +shuffle_94,7,C15orf48 +shuffle_94,8,ENSG00000235126 +shuffle_94,9,LINC01997 +shuffle_94,10,CUX1 +shuffle_94,11,LINC03017 +shuffle_94,12,NBR2 +shuffle_94,13,LRFN5 +shuffle_94,14,KRT8 +shuffle_94,15,GPR33 +shuffle_94,16,CNTN2 +shuffle_94,17,LINC02252 +shuffle_94,18,ENSG00000255457 +shuffle_94,19,RN7SL596P +shuffle_94,20,TMEM255A +shuffle_94,21,POLR1G +shuffle_94,22,SLC6A7 +shuffle_94,23,INAVA +shuffle_95,1,ENSG00000286848 +shuffle_95,2,HDGFL3P1 +shuffle_95,3,CYLC2 +shuffle_95,4,ENSG00000259097 +shuffle_95,5,MRFAP1L1 +shuffle_95,6,NDUFA6-DT +shuffle_95,7,CDH20 +shuffle_95,8,MOGAT3 +shuffle_95,9,ENSG00000254263 +shuffle_95,10,GATA3 +shuffle_95,11,ACADVL +shuffle_95,12,ARID4A +shuffle_95,13,ALDH1A1 +shuffle_95,14,ENSG00000253427 +shuffle_95,15,FAAH2 +shuffle_95,16,ENSG00000287308 +shuffle_95,17,ENSG00000278153 +shuffle_95,18,ENSG00000287057 +shuffle_95,19,SPMIP9 +shuffle_95,20,ENSG00000248203 +shuffle_95,21,INO80B +shuffle_95,22,ENSG00000271519 +shuffle_95,23,LINC01656 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..1525ab9 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,HSP90AA1,0.85228352,0.9898886191746432,23,patience,0.01064,0.01308,1.0 +shuffle_91,IGSF23,0.4978203733333334,0.5157621354126981,23,max_iterations,0.0316,0.08696,0.0 +shuffle_92,RBM5-AS1,0.5068078222222222,0.5101262831490928,23,max_iterations,0.03092,0.08432,0.0 +shuffle_93,NCOA4,0.5026190222222222,0.5104083697872537,23,max_iterations,0.02768,0.0434,0.0 +shuffle_94,PRCD,0.5036423822222222,0.5065250486072422,23,max_iterations,0.0286,0.03716,0.022222222222222223 +shuffle_95,ENSG00000286848,0.5021501866666667,0.5113565909651974,23,max_iterations,0.03052,0.15824,0.0 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_96-100/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/excitatory/null_batches/shuffles_96-100/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..a21f7c8 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_96-100/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,128 @@ +run,iteration,flip_rate +observed,2,0.03952 +observed,3,0.01552 +observed,4,0.02584 +observed,5,0.04712 +observed,6,0.0228 +observed,7,0.03388 +observed,8,0.03188 +observed,9,0.04292 +observed,10,0.0194 +observed,11,0.01896 +observed,12,0.0214 +observed,13,0.01064 +observed,14,0.01084 +observed,15,0.01124 +observed,16,0.01756 +observed,17,0.01928 +observed,18,0.01688 +observed,19,0.01312 +observed,20,0.01424 +observed,21,0.01368 +observed,22,0.0138 +observed,23,0.01308 +shuffle_96,2,0.07192 +shuffle_96,3,0.06504 +shuffle_96,4,0.0624 +shuffle_96,5,0.05492 +shuffle_96,6,0.05036 +shuffle_96,7,0.04812 +shuffle_96,8,0.045 +shuffle_96,9,0.04068 +shuffle_96,10,0.03908 +shuffle_96,11,0.03544 +shuffle_96,12,0.03284 +shuffle_96,13,0.028 +shuffle_96,14,0.2928 +shuffle_96,15,0.08048 +shuffle_96,16,0.06532 +shuffle_96,17,0.04868 +shuffle_96,18,0.04108 +shuffle_96,19,0.0798 +shuffle_96,20,0.0408 +shuffle_96,21,0.04044 +shuffle_96,22,0.0872 +shuffle_97,2,0.06764 +shuffle_97,3,0.06544 +shuffle_97,4,0.06308 +shuffle_97,5,0.05604 +shuffle_97,6,0.04944 +shuffle_97,7,0.04968 +shuffle_97,8,0.04376 +shuffle_97,9,0.04044 +shuffle_97,10,0.03616 +shuffle_97,11,0.03496 +shuffle_97,12,0.0314 +shuffle_97,13,0.02932 +shuffle_97,14,0.28456 +shuffle_97,15,0.0542 +shuffle_97,16,0.06548 +shuffle_97,17,0.0504 +shuffle_97,18,0.051 +shuffle_97,19,0.03964 +shuffle_97,20,0.03672 +shuffle_97,21,0.03876 +shuffle_97,22,0.06348 +shuffle_98,2,0.07184 +shuffle_98,3,0.0654 +shuffle_98,4,0.06004 +shuffle_98,5,0.0546 +shuffle_98,6,0.05176 +shuffle_98,7,0.04788 +shuffle_98,8,0.03904 +shuffle_98,9,0.04284 +shuffle_98,10,0.03904 +shuffle_98,11,0.03596 +shuffle_98,12,0.032 +shuffle_98,13,0.02616 +shuffle_98,14,0.02424 +shuffle_98,15,0.29328 +shuffle_98,16,0.08072 +shuffle_98,17,0.0518 +shuffle_98,18,0.0384 +shuffle_98,19,0.0464 +shuffle_98,20,0.03876 +shuffle_98,21,0.03656 +shuffle_98,22,0.08708 +shuffle_99,2,0.06964 +shuffle_99,3,0.06428 +shuffle_99,4,0.06288 +shuffle_99,5,0.05532 +shuffle_99,6,0.0486 +shuffle_99,7,0.04872 +shuffle_99,8,0.04628 +shuffle_99,9,0.0418 +shuffle_99,10,0.03868 +shuffle_99,11,0.03252 +shuffle_99,12,0.03208 +shuffle_99,13,0.02932 +shuffle_99,14,0.28836 +shuffle_99,15,0.0388 +shuffle_99,16,0.12476 +shuffle_99,17,0.04012 +shuffle_99,18,0.07464 +shuffle_99,19,0.09316 +shuffle_99,20,0.07104 +shuffle_99,21,0.04224 +shuffle_99,22,0.0416 +shuffle_100,2,0.0704 +shuffle_100,3,0.0684 +shuffle_100,4,0.05884 +shuffle_100,5,0.05556 +shuffle_100,6,0.04704 +shuffle_100,7,0.04608 +shuffle_100,8,0.04348 +shuffle_100,9,0.042 +shuffle_100,10,0.037 +shuffle_100,11,0.03264 +shuffle_100,12,0.03268 +shuffle_100,13,0.03212 +shuffle_100,14,0.25844 +shuffle_100,15,0.12312 +shuffle_100,16,0.06084 +shuffle_100,17,0.10296 +shuffle_100,18,0.11124 +shuffle_100,19,0.03372 +shuffle_100,20,0.03228 +shuffle_100,21,0.03128 +shuffle_100,22,0.03712 diff --git a/manuscript/robustness/excitatory/null_batches/shuffles_96-100/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/excitatory/null_batches/shuffles_96-100/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..6828807 --- /dev/null +++ b/manuscript/robustness/excitatory/null_batches/shuffles_96-100/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,139 @@ +run,rank,gene +observed,1,HSP90AA1 +observed,2,CNTNAP2 +observed,3,SNHG14 +observed,4,MAP1B +observed,5,LUC7L3 +observed,6,LRFN5 +observed,7,COPG2 +observed,8,TENM2 +observed,9,RBM25 +observed,10,SRSF11 +observed,11,PNISR +observed,12,AKAP9 +observed,13,LRP1B +observed,14,DPP10 +observed,15,CDH12 +observed,16,ANKRD26 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+7,coexpression,23,TTest,2,50,0,3 +7,coexpression,23,LogReg,1,50,1,50 +7,coexpression,23,recursieve,0,24,0,24 +7,coexpression,24,Wilcoxon,2,34,0,3 +7,coexpression,24,TTest,2,50,0,3 +7,coexpression,24,LogReg,1,50,1,50 +7,coexpression,24,recursieve,0,50,0,50 +7,null,0,Wilcoxon,0,22,0,0 +7,null,0,TTest,0,50,0,0 +7,null,0,LogReg,0,50,0,50 +7,null,0,recursieve,0,50,0,50 +7,null,1,Wilcoxon,0,22,0,0 +7,null,1,TTest,0,50,0,0 +7,null,1,LogReg,0,50,0,50 +7,null,1,recursieve,0,40,0,40 +7,null,2,Wilcoxon,0,22,0,0 +7,null,2,TTest,0,50,0,0 +7,null,2,LogReg,0,50,0,50 +7,null,2,recursieve,0,32,0,32 +7,null,3,Wilcoxon,0,22,0,0 +7,null,3,TTest,0,50,0,0 +7,null,3,LogReg,0,50,0,50 +7,null,3,recursieve,0,49,0,49 +7,null,4,Wilcoxon,0,22,0,0 +7,null,4,TTest,0,50,0,0 +7,null,4,LogReg,0,50,0,50 +7,null,4,recursieve,0,37,0,37 +7,null,5,Wilcoxon,0,22,0,0 +7,null,5,TTest,0,50,0,0 +7,null,5,LogReg,0,50,0,50 +7,null,5,recursieve,0,50,0,50 +7,null,6,Wilcoxon,0,22,0,0 +7,null,6,TTest,0,50,0,0 +7,null,6,LogReg,0,50,0,50 +7,null,6,recursieve,0,27,0,27 +7,null,7,Wilcoxon,0,22,0,0 +7,null,7,TTest,0,50,0,0 +7,null,7,LogReg,0,50,0,50 +7,null,7,recursieve,0,45,0,45 +7,null,8,Wilcoxon,0,22,0,0 +7,null,8,TTest,0,50,0,0 +7,null,8,LogReg,0,50,0,50 +7,null,8,recursieve,0,50,0,50 +7,null,9,Wilcoxon,0,22,0,0 +7,null,9,TTest,0,50,0,0 +7,null,9,LogReg,0,50,0,50 +7,null,9,recursieve,0,18,0,18 +7,null,10,Wilcoxon,0,22,0,0 +7,null,10,TTest,0,50,0,0 +7,null,10,LogReg,0,50,0,50 +7,null,10,recursieve,0,50,0,50 +7,null,11,Wilcoxon,0,22,0,0 +7,null,11,TTest,0,50,0,0 +7,null,11,LogReg,0,50,0,50 +7,null,11,recursieve,0,17,0,17 +7,null,12,Wilcoxon,0,22,0,0 +7,null,12,TTest,0,50,0,0 +7,null,12,LogReg,0,50,0,50 +7,null,12,recursieve,0,23,0,23 +7,null,13,Wilcoxon,0,22,0,0 +7,null,13,TTest,0,50,0,0 +7,null,13,LogReg,0,50,0,50 +7,null,13,recursieve,0,39,0,39 +7,null,14,Wilcoxon,0,22,0,0 +7,null,14,TTest,0,50,0,0 +7,null,14,LogReg,0,50,0,50 +7,null,14,recursieve,0,48,0,48 +7,null,15,Wilcoxon,0,22,0,0 +7,null,15,TTest,0,50,0,0 +7,null,15,LogReg,0,50,0,50 +7,null,15,recursieve,0,48,0,48 +7,null,16,Wilcoxon,0,22,0,0 +7,null,16,TTest,0,50,0,0 +7,null,16,LogReg,0,50,0,50 +7,null,16,recursieve,0,13,0,13 +7,null,17,Wilcoxon,0,22,0,0 +7,null,17,TTest,0,50,0,0 +7,null,17,LogReg,0,50,0,50 +7,null,17,recursieve,0,42,0,42 +7,null,18,Wilcoxon,0,22,0,0 +7,null,18,TTest,0,50,0,0 +7,null,18,LogReg,0,50,0,50 +7,null,18,recursieve,0,50,0,50 +7,null,19,Wilcoxon,0,22,0,0 +7,null,19,TTest,0,50,0,0 +7,null,19,LogReg,0,50,0,50 +7,null,19,recursieve,0,48,0,48 +7,null,20,Wilcoxon,0,22,0,0 +7,null,20,TTest,0,50,0,0 +7,null,20,LogReg,0,50,0,50 +7,null,20,recursieve,0,32,0,32 +7,null,21,Wilcoxon,0,22,0,0 +7,null,21,TTest,0,50,0,0 +7,null,21,LogReg,0,50,0,50 +7,null,21,recursieve,0,49,0,49 +7,null,22,Wilcoxon,0,22,0,0 +7,null,22,TTest,0,50,0,0 +7,null,22,LogReg,0,50,0,50 +7,null,22,recursieve,0,31,0,31 +7,null,23,Wilcoxon,0,22,0,0 +7,null,23,TTest,0,50,0,0 +7,null,23,LogReg,0,50,0,50 +7,null,23,recursieve,0,36,0,36 +7,null,24,Wilcoxon,0,22,0,0 +7,null,24,TTest,0,50,0,0 +7,null,24,LogReg,0,50,0,50 +7,null,24,recursieve,0,25,0,25 +7,skew,0,Wilcoxon,0,32,0,3 +7,skew,0,TTest,1,50,0,3 +7,skew,0,LogReg,0,50,0,50 +7,skew,0,recursieve,0,50,0,50 +7,skew,1,Wilcoxon,0,32,0,3 +7,skew,1,TTest,1,50,0,3 +7,skew,1,LogReg,0,50,0,50 +7,skew,1,recursieve,1,47,1,47 +7,skew,2,Wilcoxon,0,32,0,3 +7,skew,2,TTest,1,50,0,3 +7,skew,2,LogReg,0,50,0,50 +7,skew,2,recursieve,10,50,10,50 +7,skew,3,Wilcoxon,0,32,0,3 +7,skew,3,TTest,1,50,0,3 +7,skew,3,LogReg,0,50,0,50 +7,skew,3,recursieve,6,50,6,50 +7,skew,4,Wilcoxon,0,32,0,3 +7,skew,4,TTest,1,50,0,3 +7,skew,4,LogReg,0,50,0,50 +7,skew,4,recursieve,10,50,10,50 +7,skew,5,Wilcoxon,0,32,0,3 +7,skew,5,TTest,1,50,0,3 +7,skew,5,LogReg,0,50,0,50 +7,skew,5,recursieve,10,50,10,50 +7,skew,6,Wilcoxon,0,32,0,3 +7,skew,6,TTest,1,50,0,3 +7,skew,6,LogReg,0,50,0,50 +7,skew,6,recursieve,3,50,3,50 +7,skew,7,Wilcoxon,0,32,0,3 +7,skew,7,TTest,1,50,0,3 +7,skew,7,LogReg,0,50,0,50 +7,skew,7,recursieve,1,50,1,50 +7,skew,8,Wilcoxon,0,32,0,3 +7,skew,8,TTest,1,50,0,3 +7,skew,8,LogReg,0,50,0,50 +7,skew,8,recursieve,10,46,10,46 +7,skew,9,Wilcoxon,0,32,0,3 +7,skew,9,TTest,1,50,0,3 +7,skew,9,LogReg,0,50,0,50 +7,skew,9,recursieve,1,33,1,33 +7,skew,10,Wilcoxon,0,32,0,3 +7,skew,10,TTest,1,50,0,3 +7,skew,10,LogReg,0,50,0,50 +7,skew,10,recursieve,0,40,0,40 +7,skew,11,Wilcoxon,0,32,0,3 +7,skew,11,TTest,1,50,0,3 +7,skew,11,LogReg,0,50,0,50 +7,skew,11,recursieve,1,27,1,27 +7,skew,12,Wilcoxon,0,32,0,3 +7,skew,12,TTest,1,50,0,3 +7,skew,12,LogReg,0,50,0,50 +7,skew,12,recursieve,0,27,0,27 +7,skew,13,Wilcoxon,0,32,0,3 +7,skew,13,TTest,1,50,0,3 +7,skew,13,LogReg,0,50,0,50 +7,skew,13,recursieve,2,50,2,50 +7,skew,14,Wilcoxon,0,32,0,3 +7,skew,14,TTest,1,50,0,3 +7,skew,14,LogReg,0,50,0,50 +7,skew,14,recursieve,0,27,0,27 +7,skew,15,Wilcoxon,0,32,0,3 +7,skew,15,TTest,1,50,0,3 +7,skew,15,LogReg,0,50,0,50 +7,skew,15,recursieve,2,50,2,50 +7,skew,16,Wilcoxon,0,32,0,3 +7,skew,16,TTest,1,50,0,3 +7,skew,16,LogReg,0,50,0,50 +7,skew,16,recursieve,2,39,2,39 +7,skew,17,Wilcoxon,0,32,0,3 +7,skew,17,TTest,1,50,0,3 +7,skew,17,LogReg,0,50,0,50 +7,skew,17,recursieve,10,50,10,50 +7,skew,18,Wilcoxon,0,32,0,3 +7,skew,18,TTest,1,50,0,3 +7,skew,18,LogReg,0,50,0,50 +7,skew,18,recursieve,0,29,0,29 +7,skew,19,Wilcoxon,0,32,0,3 +7,skew,19,TTest,1,50,0,3 +7,skew,19,LogReg,0,50,0,50 +7,skew,19,recursieve,10,44,10,44 +7,skew,20,Wilcoxon,0,32,0,3 +7,skew,20,TTest,1,50,0,3 +7,skew,20,LogReg,0,50,0,50 +7,skew,20,recursieve,0,49,0,49 +7,skew,21,Wilcoxon,0,32,0,3 +7,skew,21,TTest,1,50,0,3 +7,skew,21,LogReg,0,50,0,50 +7,skew,21,recursieve,6,46,6,46 +7,skew,22,Wilcoxon,0,32,0,3 +7,skew,22,TTest,1,50,0,3 +7,skew,22,LogReg,0,50,0,50 +7,skew,22,recursieve,0,42,0,42 +7,skew,23,Wilcoxon,0,32,0,3 +7,skew,23,TTest,1,50,0,3 +7,skew,23,LogReg,0,50,0,50 +7,skew,23,recursieve,10,43,10,43 +7,skew,24,Wilcoxon,0,32,0,3 +7,skew,24,TTest,1,50,0,3 +7,skew,24,LogReg,0,50,0,50 +7,skew,24,recursieve,5,50,5,50 +7,trimodal,0,Wilcoxon,5,37,0,3 +7,trimodal,0,TTest,0,50,0,3 +7,trimodal,0,LogReg,0,50,0,50 +7,trimodal,0,recursieve,10,50,10,50 +7,trimodal,1,Wilcoxon,5,37,0,3 +7,trimodal,1,TTest,0,50,0,3 +7,trimodal,1,LogReg,0,50,0,50 +7,trimodal,1,recursieve,1,50,1,50 +7,trimodal,2,Wilcoxon,5,37,0,3 +7,trimodal,2,TTest,0,50,0,3 +7,trimodal,2,LogReg,0,50,0,50 +7,trimodal,2,recursieve,10,47,10,47 +7,trimodal,3,Wilcoxon,5,37,0,3 +7,trimodal,3,TTest,0,50,0,3 +7,trimodal,3,LogReg,0,50,0,50 +7,trimodal,3,recursieve,10,50,10,50 +7,trimodal,4,Wilcoxon,5,37,0,3 +7,trimodal,4,TTest,0,50,0,3 +7,trimodal,4,LogReg,0,50,0,50 +7,trimodal,4,recursieve,0,48,0,48 +7,trimodal,5,Wilcoxon,5,37,0,3 +7,trimodal,5,TTest,0,50,0,3 +7,trimodal,5,LogReg,0,50,0,50 +7,trimodal,5,recursieve,10,35,10,35 +7,trimodal,6,Wilcoxon,5,37,0,3 +7,trimodal,6,TTest,0,50,0,3 +7,trimodal,6,LogReg,0,50,0,50 +7,trimodal,6,recursieve,10,50,10,50 +7,trimodal,7,Wilcoxon,5,37,0,3 +7,trimodal,7,TTest,0,50,0,3 +7,trimodal,7,LogReg,0,50,0,50 +7,trimodal,7,recursieve,10,43,10,43 +7,trimodal,8,Wilcoxon,5,37,0,3 +7,trimodal,8,TTest,0,50,0,3 +7,trimodal,8,LogReg,0,50,0,50 +7,trimodal,8,recursieve,5,47,5,47 +7,trimodal,9,Wilcoxon,5,37,0,3 +7,trimodal,9,TTest,0,50,0,3 +7,trimodal,9,LogReg,0,50,0,50 +7,trimodal,9,recursieve,1,50,1,50 +7,trimodal,10,Wilcoxon,5,37,0,3 +7,trimodal,10,TTest,0,50,0,3 +7,trimodal,10,LogReg,0,50,0,50 +7,trimodal,10,recursieve,3,50,3,50 +7,trimodal,11,Wilcoxon,5,37,0,3 +7,trimodal,11,TTest,0,50,0,3 +7,trimodal,11,LogReg,0,50,0,50 +7,trimodal,11,recursieve,1,41,1,41 +7,trimodal,12,Wilcoxon,5,37,0,3 +7,trimodal,12,TTest,0,50,0,3 +7,trimodal,12,LogReg,0,50,0,50 +7,trimodal,12,recursieve,1,26,1,26 +7,trimodal,13,Wilcoxon,5,37,0,3 +7,trimodal,13,TTest,0,50,0,3 +7,trimodal,13,LogReg,0,50,0,50 +7,trimodal,13,recursieve,1,35,1,35 +7,trimodal,14,Wilcoxon,5,37,0,3 +7,trimodal,14,TTest,0,50,0,3 +7,trimodal,14,LogReg,0,50,0,50 +7,trimodal,14,recursieve,10,29,10,29 +7,trimodal,15,Wilcoxon,5,37,0,3 +7,trimodal,15,TTest,0,50,0,3 +7,trimodal,15,LogReg,0,50,0,50 +7,trimodal,15,recursieve,10,50,10,50 +7,trimodal,16,Wilcoxon,5,37,0,3 +7,trimodal,16,TTest,0,50,0,3 +7,trimodal,16,LogReg,0,50,0,50 +7,trimodal,16,recursieve,5,45,5,45 +7,trimodal,17,Wilcoxon,5,37,0,3 +7,trimodal,17,TTest,0,50,0,3 +7,trimodal,17,LogReg,0,50,0,50 +7,trimodal,17,recursieve,4,42,4,42 +7,trimodal,18,Wilcoxon,5,37,0,3 +7,trimodal,18,TTest,0,50,0,3 +7,trimodal,18,LogReg,0,50,0,50 +7,trimodal,18,recursieve,1,25,1,25 +7,trimodal,19,Wilcoxon,5,37,0,3 +7,trimodal,19,TTest,0,50,0,3 +7,trimodal,19,LogReg,0,50,0,50 +7,trimodal,19,recursieve,7,50,7,50 +7,trimodal,20,Wilcoxon,5,37,0,3 +7,trimodal,20,TTest,0,50,0,3 +7,trimodal,20,LogReg,0,50,0,50 +7,trimodal,20,recursieve,10,41,10,41 +7,trimodal,21,Wilcoxon,5,37,0,3 +7,trimodal,21,TTest,0,50,0,3 +7,trimodal,21,LogReg,0,50,0,50 +7,trimodal,21,recursieve,10,50,10,50 +7,trimodal,22,Wilcoxon,5,37,0,3 +7,trimodal,22,TTest,0,50,0,3 +7,trimodal,22,LogReg,0,50,0,50 +7,trimodal,22,recursieve,2,35,2,35 +7,trimodal,23,Wilcoxon,5,37,0,3 +7,trimodal,23,TTest,0,50,0,3 +7,trimodal,23,LogReg,0,50,0,50 +7,trimodal,23,recursieve,10,45,10,45 +7,trimodal,24,Wilcoxon,5,37,0,3 +7,trimodal,24,TTest,0,50,0,3 +7,trimodal,24,LogReg,0,50,0,50 +7,trimodal,24,recursieve,10,43,10,43 +8,bimodal,0,Wilcoxon,0,26,0,2 +8,bimodal,0,TTest,0,50,0,3 +8,bimodal,0,LogReg,0,50,0,50 +8,bimodal,0,recursieve,1,50,1,50 +8,bimodal,1,Wilcoxon,0,26,0,2 +8,bimodal,1,TTest,0,50,0,3 +8,bimodal,1,LogReg,0,50,0,50 +8,bimodal,1,recursieve,0,29,0,29 +8,bimodal,2,Wilcoxon,0,26,0,2 +8,bimodal,2,TTest,0,50,0,3 +8,bimodal,2,LogReg,0,50,0,50 +8,bimodal,2,recursieve,0,35,0,35 +8,bimodal,3,Wilcoxon,0,26,0,2 +8,bimodal,3,TTest,0,50,0,3 +8,bimodal,3,LogReg,0,50,0,50 +8,bimodal,3,recursieve,0,46,0,46 +8,bimodal,4,Wilcoxon,0,26,0,2 +8,bimodal,4,TTest,0,50,0,3 +8,bimodal,4,LogReg,0,50,0,50 +8,bimodal,4,recursieve,0,39,0,39 +8,bimodal,5,Wilcoxon,0,26,0,2 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+13,bimodal,19,Wilcoxon,10,35,1,3 +13,bimodal,19,TTest,0,50,5,7 +13,bimodal,19,LogReg,0,50,0,50 +13,bimodal,19,recursieve,0,40,0,40 +13,bimodal,20,Wilcoxon,10,35,1,3 +13,bimodal,20,TTest,0,50,5,7 +13,bimodal,20,LogReg,0,50,0,50 +13,bimodal,20,recursieve,1,50,1,50 +13,bimodal,21,Wilcoxon,10,35,1,3 +13,bimodal,21,TTest,0,50,5,7 +13,bimodal,21,LogReg,0,50,0,50 +13,bimodal,21,recursieve,0,50,0,50 +13,bimodal,22,Wilcoxon,10,35,1,3 +13,bimodal,22,TTest,0,50,5,7 +13,bimodal,22,LogReg,0,50,0,50 +13,bimodal,22,recursieve,0,34,0,34 +13,bimodal,23,Wilcoxon,10,35,1,3 +13,bimodal,23,TTest,0,50,5,7 +13,bimodal,23,LogReg,0,50,0,50 +13,bimodal,23,recursieve,0,29,0,29 +13,bimodal,24,Wilcoxon,10,35,1,3 +13,bimodal,24,TTest,0,50,5,7 +13,bimodal,24,LogReg,0,50,0,50 +13,bimodal,24,recursieve,0,30,0,30 +13,coexpression,0,Wilcoxon,4,29,1,3 +13,coexpression,0,TTest,6,50,1,3 +13,coexpression,0,LogReg,1,50,1,50 +13,coexpression,0,recursieve,0,50,0,50 +13,coexpression,1,Wilcoxon,4,29,1,3 +13,coexpression,1,TTest,6,50,1,3 +13,coexpression,1,LogReg,1,50,1,50 +13,coexpression,1,recursieve,10,26,10,26 +13,coexpression,2,Wilcoxon,4,29,1,3 +13,coexpression,2,TTest,6,50,1,3 +13,coexpression,2,LogReg,1,50,1,50 +13,coexpression,2,recursieve,10,26,10,26 +13,coexpression,3,Wilcoxon,4,29,1,3 +13,coexpression,3,TTest,6,50,1,3 +13,coexpression,3,LogReg,1,50,1,50 +13,coexpression,3,recursieve,10,25,10,25 +13,coexpression,4,Wilcoxon,4,29,1,3 +13,coexpression,4,TTest,6,50,1,3 +13,coexpression,4,LogReg,1,50,1,50 +13,coexpression,4,recursieve,10,41,10,41 +13,coexpression,5,Wilcoxon,4,29,1,3 +13,coexpression,5,TTest,6,50,1,3 +13,coexpression,5,LogReg,1,50,1,50 +13,coexpression,5,recursieve,0,25,0,25 +13,coexpression,6,Wilcoxon,4,29,1,3 +13,coexpression,6,TTest,6,50,1,3 +13,coexpression,6,LogReg,1,50,1,50 +13,coexpression,6,recursieve,1,40,1,40 +13,coexpression,7,Wilcoxon,4,29,1,3 +13,coexpression,7,TTest,6,50,1,3 +13,coexpression,7,LogReg,1,50,1,50 +13,coexpression,7,recursieve,10,46,10,46 +13,coexpression,8,Wilcoxon,4,29,1,3 +13,coexpression,8,TTest,6,50,1,3 +13,coexpression,8,LogReg,1,50,1,50 +13,coexpression,8,recursieve,10,50,10,50 +13,coexpression,9,Wilcoxon,4,29,1,3 +13,coexpression,9,TTest,6,50,1,3 +13,coexpression,9,LogReg,1,50,1,50 +13,coexpression,9,recursieve,0,27,0,27 +13,coexpression,10,Wilcoxon,4,29,1,3 +13,coexpression,10,TTest,6,50,1,3 +13,coexpression,10,LogReg,1,50,1,50 +13,coexpression,10,recursieve,0,28,0,28 +13,coexpression,11,Wilcoxon,4,29,1,3 +13,coexpression,11,TTest,6,50,1,3 +13,coexpression,11,LogReg,1,50,1,50 +13,coexpression,11,recursieve,0,27,0,27 +13,coexpression,12,Wilcoxon,4,29,1,3 +13,coexpression,12,TTest,6,50,1,3 +13,coexpression,12,LogReg,1,50,1,50 +13,coexpression,12,recursieve,1,50,1,50 +13,coexpression,13,Wilcoxon,4,29,1,3 +13,coexpression,13,TTest,6,50,1,3 +13,coexpression,13,LogReg,1,50,1,50 +13,coexpression,13,recursieve,0,34,0,34 +13,coexpression,14,Wilcoxon,4,29,1,3 +13,coexpression,14,TTest,6,50,1,3 +13,coexpression,14,LogReg,1,50,1,50 +13,coexpression,14,recursieve,0,27,0,27 +13,coexpression,15,Wilcoxon,4,29,1,3 +13,coexpression,15,TTest,6,50,1,3 +13,coexpression,15,LogReg,1,50,1,50 +13,coexpression,15,recursieve,1,50,1,50 +13,coexpression,16,Wilcoxon,4,29,1,3 +13,coexpression,16,TTest,6,50,1,3 +13,coexpression,16,LogReg,1,50,1,50 +13,coexpression,16,recursieve,10,37,10,37 +13,coexpression,17,Wilcoxon,4,29,1,3 +13,coexpression,17,TTest,6,50,1,3 +13,coexpression,17,LogReg,1,50,1,50 +13,coexpression,17,recursieve,1,36,1,36 +13,coexpression,18,Wilcoxon,4,29,1,3 +13,coexpression,18,TTest,6,50,1,3 +13,coexpression,18,LogReg,1,50,1,50 +13,coexpression,18,recursieve,0,50,0,50 +13,coexpression,19,Wilcoxon,4,29,1,3 +13,coexpression,19,TTest,6,50,1,3 +13,coexpression,19,LogReg,1,50,1,50 +13,coexpression,19,recursieve,0,29,0,29 +13,coexpression,20,Wilcoxon,4,29,1,3 +13,coexpression,20,TTest,6,50,1,3 +13,coexpression,20,LogReg,1,50,1,50 +13,coexpression,20,recursieve,1,33,1,33 +13,coexpression,21,Wilcoxon,4,29,1,3 +13,coexpression,21,TTest,6,50,1,3 +13,coexpression,21,LogReg,1,50,1,50 +13,coexpression,21,recursieve,0,40,0,40 +13,coexpression,22,Wilcoxon,4,29,1,3 +13,coexpression,22,TTest,6,50,1,3 +13,coexpression,22,LogReg,1,50,1,50 +13,coexpression,22,recursieve,10,43,10,43 +13,coexpression,23,Wilcoxon,4,29,1,3 +13,coexpression,23,TTest,6,50,1,3 +13,coexpression,23,LogReg,1,50,1,50 +13,coexpression,23,recursieve,10,23,10,23 +13,coexpression,24,Wilcoxon,4,29,1,3 +13,coexpression,24,TTest,6,50,1,3 +13,coexpression,24,LogReg,1,50,1,50 +13,coexpression,24,recursieve,0,20,0,20 +13,null,0,Wilcoxon,0,34,0,0 +13,null,0,TTest,0,50,0,0 +13,null,0,LogReg,0,50,0,50 +13,null,0,recursieve,0,12,0,12 +13,null,1,Wilcoxon,0,34,0,0 +13,null,1,TTest,0,50,0,0 +13,null,1,LogReg,0,50,0,50 +13,null,1,recursieve,0,50,0,50 +13,null,2,Wilcoxon,0,34,0,0 +13,null,2,TTest,0,50,0,0 +13,null,2,LogReg,0,50,0,50 +13,null,2,recursieve,0,44,0,44 +13,null,3,Wilcoxon,0,34,0,0 +13,null,3,TTest,0,50,0,0 +13,null,3,LogReg,0,50,0,50 +13,null,3,recursieve,0,32,0,32 +13,null,4,Wilcoxon,0,34,0,0 +13,null,4,TTest,0,50,0,0 +13,null,4,LogReg,0,50,0,50 +13,null,4,recursieve,0,50,0,50 +13,null,5,Wilcoxon,0,34,0,0 +13,null,5,TTest,0,50,0,0 +13,null,5,LogReg,0,50,0,50 +13,null,5,recursieve,0,40,0,40 +13,null,6,Wilcoxon,0,34,0,0 +13,null,6,TTest,0,50,0,0 +13,null,6,LogReg,0,50,0,50 +13,null,6,recursieve,0,45,0,45 +13,null,7,Wilcoxon,0,34,0,0 +13,null,7,TTest,0,50,0,0 +13,null,7,LogReg,0,50,0,50 +13,null,7,recursieve,0,50,0,50 +13,null,8,Wilcoxon,0,34,0,0 +13,null,8,TTest,0,50,0,0 +13,null,8,LogReg,0,50,0,50 +13,null,8,recursieve,0,34,0,34 +13,null,9,Wilcoxon,0,34,0,0 +13,null,9,TTest,0,50,0,0 +13,null,9,LogReg,0,50,0,50 +13,null,9,recursieve,0,26,0,26 +13,null,10,Wilcoxon,0,34,0,0 +13,null,10,TTest,0,50,0,0 +13,null,10,LogReg,0,50,0,50 +13,null,10,recursieve,0,25,0,25 +13,null,11,Wilcoxon,0,34,0,0 +13,null,11,TTest,0,50,0,0 +13,null,11,LogReg,0,50,0,50 +13,null,11,recursieve,0,49,0,49 +13,null,12,Wilcoxon,0,34,0,0 +13,null,12,TTest,0,50,0,0 +13,null,12,LogReg,0,50,0,50 +13,null,12,recursieve,0,50,0,50 +13,null,13,Wilcoxon,0,34,0,0 +13,null,13,TTest,0,50,0,0 +13,null,13,LogReg,0,50,0,50 +13,null,13,recursieve,0,50,0,50 +13,null,14,Wilcoxon,0,34,0,0 +13,null,14,TTest,0,50,0,0 +13,null,14,LogReg,0,50,0,50 +13,null,14,recursieve,0,50,0,50 +13,null,15,Wilcoxon,0,34,0,0 +13,null,15,TTest,0,50,0,0 +13,null,15,LogReg,0,50,0,50 +13,null,15,recursieve,0,50,0,50 +13,null,16,Wilcoxon,0,34,0,0 +13,null,16,TTest,0,50,0,0 +13,null,16,LogReg,0,50,0,50 +13,null,16,recursieve,0,50,0,50 +13,null,17,Wilcoxon,0,34,0,0 +13,null,17,TTest,0,50,0,0 +13,null,17,LogReg,0,50,0,50 +13,null,17,recursieve,0,47,0,47 +13,null,18,Wilcoxon,0,34,0,0 +13,null,18,TTest,0,50,0,0 +13,null,18,LogReg,0,50,0,50 +13,null,18,recursieve,0,42,0,42 +13,null,19,Wilcoxon,0,34,0,0 +13,null,19,TTest,0,50,0,0 +13,null,19,LogReg,0,50,0,50 +13,null,19,recursieve,0,48,0,48 +13,null,20,Wilcoxon,0,34,0,0 +13,null,20,TTest,0,50,0,0 +13,null,20,LogReg,0,50,0,50 +13,null,20,recursieve,0,50,0,50 +13,null,21,Wilcoxon,0,34,0,0 +13,null,21,TTest,0,50,0,0 +13,null,21,LogReg,0,50,0,50 +13,null,21,recursieve,0,50,0,50 +13,null,22,Wilcoxon,0,34,0,0 +13,null,22,TTest,0,50,0,0 +13,null,22,LogReg,0,50,0,50 +13,null,22,recursieve,0,50,0,50 +13,null,23,Wilcoxon,0,34,0,0 +13,null,23,TTest,0,50,0,0 +13,null,23,LogReg,0,50,0,50 +13,null,23,recursieve,0,25,0,25 +13,null,24,Wilcoxon,0,34,0,0 +13,null,24,TTest,0,50,0,0 +13,null,24,LogReg,0,50,0,50 +13,null,24,recursieve,0,42,0,42 +13,skew,0,Wilcoxon,1,26,0,2 +13,skew,0,TTest,0,50,0,2 +13,skew,0,LogReg,0,50,0,50 +13,skew,0,recursieve,0,49,0,49 +13,skew,1,Wilcoxon,1,26,0,2 +13,skew,1,TTest,0,50,0,2 +13,skew,1,LogReg,0,50,0,50 +13,skew,1,recursieve,0,47,0,47 +13,skew,2,Wilcoxon,1,26,0,2 +13,skew,2,TTest,0,50,0,2 +13,skew,2,LogReg,0,50,0,50 +13,skew,2,recursieve,0,38,0,38 +13,skew,3,Wilcoxon,1,26,0,2 +13,skew,3,TTest,0,50,0,2 +13,skew,3,LogReg,0,50,0,50 +13,skew,3,recursieve,0,24,0,24 +13,skew,4,Wilcoxon,1,26,0,2 +13,skew,4,TTest,0,50,0,2 +13,skew,4,LogReg,0,50,0,50 +13,skew,4,recursieve,0,34,0,34 +13,skew,5,Wilcoxon,1,26,0,2 +13,skew,5,TTest,0,50,0,2 +13,skew,5,LogReg,0,50,0,50 +13,skew,5,recursieve,0,30,0,30 +13,skew,6,Wilcoxon,1,26,0,2 +13,skew,6,TTest,0,50,0,2 +13,skew,6,LogReg,0,50,0,50 +13,skew,6,recursieve,0,45,0,45 +13,skew,7,Wilcoxon,1,26,0,2 +13,skew,7,TTest,0,50,0,2 +13,skew,7,LogReg,0,50,0,50 +13,skew,7,recursieve,0,49,0,49 +13,skew,8,Wilcoxon,1,26,0,2 +13,skew,8,TTest,0,50,0,2 +13,skew,8,LogReg,0,50,0,50 +13,skew,8,recursieve,0,19,0,19 +13,skew,9,Wilcoxon,1,26,0,2 +13,skew,9,TTest,0,50,0,2 +13,skew,9,LogReg,0,50,0,50 +13,skew,9,recursieve,0,42,0,42 +13,skew,10,Wilcoxon,1,26,0,2 +13,skew,10,TTest,0,50,0,2 +13,skew,10,LogReg,0,50,0,50 +13,skew,10,recursieve,0,50,0,50 +13,skew,11,Wilcoxon,1,26,0,2 +13,skew,11,TTest,0,50,0,2 +13,skew,11,LogReg,0,50,0,50 +13,skew,11,recursieve,0,42,0,42 +13,skew,12,Wilcoxon,1,26,0,2 +13,skew,12,TTest,0,50,0,2 +13,skew,12,LogReg,0,50,0,50 +13,skew,12,recursieve,0,50,0,50 +13,skew,13,Wilcoxon,1,26,0,2 +13,skew,13,TTest,0,50,0,2 +13,skew,13,LogReg,0,50,0,50 +13,skew,13,recursieve,0,14,0,14 +13,skew,14,Wilcoxon,1,26,0,2 +13,skew,14,TTest,0,50,0,2 +13,skew,14,LogReg,0,50,0,50 +13,skew,14,recursieve,0,50,0,50 +13,skew,15,Wilcoxon,1,26,0,2 +13,skew,15,TTest,0,50,0,2 +13,skew,15,LogReg,0,50,0,50 +13,skew,15,recursieve,0,42,0,42 +13,skew,16,Wilcoxon,1,26,0,2 +13,skew,16,TTest,0,50,0,2 +13,skew,16,LogReg,0,50,0,50 +13,skew,16,recursieve,0,16,0,16 +13,skew,17,Wilcoxon,1,26,0,2 +13,skew,17,TTest,0,50,0,2 +13,skew,17,LogReg,0,50,0,50 +13,skew,17,recursieve,0,50,0,50 +13,skew,18,Wilcoxon,1,26,0,2 +13,skew,18,TTest,0,50,0,2 +13,skew,18,LogReg,0,50,0,50 +13,skew,18,recursieve,0,27,0,27 +13,skew,19,Wilcoxon,1,26,0,2 +13,skew,19,TTest,0,50,0,2 +13,skew,19,LogReg,0,50,0,50 +13,skew,19,recursieve,0,50,0,50 +13,skew,20,Wilcoxon,1,26,0,2 +13,skew,20,TTest,0,50,0,2 +13,skew,20,LogReg,0,50,0,50 +13,skew,20,recursieve,0,38,0,38 +13,skew,21,Wilcoxon,1,26,0,2 +13,skew,21,TTest,0,50,0,2 +13,skew,21,LogReg,0,50,0,50 +13,skew,21,recursieve,0,21,0,21 +13,skew,22,Wilcoxon,1,26,0,2 +13,skew,22,TTest,0,50,0,2 +13,skew,22,LogReg,0,50,0,50 +13,skew,22,recursieve,0,45,0,45 +13,skew,23,Wilcoxon,1,26,0,2 +13,skew,23,TTest,0,50,0,2 +13,skew,23,LogReg,0,50,0,50 +13,skew,23,recursieve,0,50,0,50 +13,skew,24,Wilcoxon,1,26,0,2 +13,skew,24,TTest,0,50,0,2 +13,skew,24,LogReg,0,50,0,50 +13,skew,24,recursieve,0,21,0,21 +13,trimodal,0,Wilcoxon,5,30,0,2 +13,trimodal,0,TTest,0,50,1,3 +13,trimodal,0,LogReg,0,50,0,50 +13,trimodal,0,recursieve,0,39,0,39 +13,trimodal,1,Wilcoxon,5,30,0,2 +13,trimodal,1,TTest,0,50,1,3 +13,trimodal,1,LogReg,0,50,0,50 +13,trimodal,1,recursieve,0,50,0,50 +13,trimodal,2,Wilcoxon,5,30,0,2 +13,trimodal,2,TTest,0,50,1,3 +13,trimodal,2,LogReg,0,50,0,50 +13,trimodal,2,recursieve,0,46,0,46 +13,trimodal,3,Wilcoxon,5,30,0,2 +13,trimodal,3,TTest,0,50,1,3 +13,trimodal,3,LogReg,0,50,0,50 +13,trimodal,3,recursieve,0,28,0,28 +13,trimodal,4,Wilcoxon,5,30,0,2 +13,trimodal,4,TTest,0,50,1,3 +13,trimodal,4,LogReg,0,50,0,50 +13,trimodal,4,recursieve,0,22,0,22 +13,trimodal,5,Wilcoxon,5,30,0,2 +13,trimodal,5,TTest,0,50,1,3 +13,trimodal,5,LogReg,0,50,0,50 +13,trimodal,5,recursieve,0,42,0,42 +13,trimodal,6,Wilcoxon,5,30,0,2 +13,trimodal,6,TTest,0,50,1,3 +13,trimodal,6,LogReg,0,50,0,50 +13,trimodal,6,recursieve,0,42,0,42 +13,trimodal,7,Wilcoxon,5,30,0,2 +13,trimodal,7,TTest,0,50,1,3 +13,trimodal,7,LogReg,0,50,0,50 +13,trimodal,7,recursieve,0,23,0,23 +13,trimodal,8,Wilcoxon,5,30,0,2 +13,trimodal,8,TTest,0,50,1,3 +13,trimodal,8,LogReg,0,50,0,50 +13,trimodal,8,recursieve,0,41,0,41 +13,trimodal,9,Wilcoxon,5,30,0,2 +13,trimodal,9,TTest,0,50,1,3 +13,trimodal,9,LogReg,0,50,0,50 +13,trimodal,9,recursieve,0,25,0,25 +13,trimodal,10,Wilcoxon,5,30,0,2 +13,trimodal,10,TTest,0,50,1,3 +13,trimodal,10,LogReg,0,50,0,50 +13,trimodal,10,recursieve,0,50,0,50 +13,trimodal,11,Wilcoxon,5,30,0,2 +13,trimodal,11,TTest,0,50,1,3 +13,trimodal,11,LogReg,0,50,0,50 +13,trimodal,11,recursieve,0,50,0,50 +13,trimodal,12,Wilcoxon,5,30,0,2 +13,trimodal,12,TTest,0,50,1,3 +13,trimodal,12,LogReg,0,50,0,50 +13,trimodal,12,recursieve,0,13,0,13 +13,trimodal,13,Wilcoxon,5,30,0,2 +13,trimodal,13,TTest,0,50,1,3 +13,trimodal,13,LogReg,0,50,0,50 +13,trimodal,13,recursieve,0,41,0,41 +13,trimodal,14,Wilcoxon,5,30,0,2 +13,trimodal,14,TTest,0,50,1,3 +13,trimodal,14,LogReg,0,50,0,50 +13,trimodal,14,recursieve,0,31,0,31 +13,trimodal,15,Wilcoxon,5,30,0,2 +13,trimodal,15,TTest,0,50,1,3 +13,trimodal,15,LogReg,0,50,0,50 +13,trimodal,15,recursieve,0,26,0,26 +13,trimodal,16,Wilcoxon,5,30,0,2 +13,trimodal,16,TTest,0,50,1,3 +13,trimodal,16,LogReg,0,50,0,50 +13,trimodal,16,recursieve,0,50,0,50 +13,trimodal,17,Wilcoxon,5,30,0,2 +13,trimodal,17,TTest,0,50,1,3 +13,trimodal,17,LogReg,0,50,0,50 +13,trimodal,17,recursieve,0,34,0,34 +13,trimodal,18,Wilcoxon,5,30,0,2 +13,trimodal,18,TTest,0,50,1,3 +13,trimodal,18,LogReg,0,50,0,50 +13,trimodal,18,recursieve,0,16,0,16 +13,trimodal,19,Wilcoxon,5,30,0,2 +13,trimodal,19,TTest,0,50,1,3 +13,trimodal,19,LogReg,0,50,0,50 +13,trimodal,19,recursieve,0,50,0,50 +13,trimodal,20,Wilcoxon,5,30,0,2 +13,trimodal,20,TTest,0,50,1,3 +13,trimodal,20,LogReg,0,50,0,50 +13,trimodal,20,recursieve,0,45,0,45 +13,trimodal,21,Wilcoxon,5,30,0,2 +13,trimodal,21,TTest,0,50,1,3 +13,trimodal,21,LogReg,0,50,0,50 +13,trimodal,21,recursieve,0,50,0,50 +13,trimodal,22,Wilcoxon,5,30,0,2 +13,trimodal,22,TTest,0,50,1,3 +13,trimodal,22,LogReg,0,50,0,50 +13,trimodal,22,recursieve,0,13,0,13 +13,trimodal,23,Wilcoxon,5,30,0,2 +13,trimodal,23,TTest,0,50,1,3 +13,trimodal,23,LogReg,0,50,0,50 +13,trimodal,23,recursieve,0,13,0,13 +13,trimodal,24,Wilcoxon,5,30,0,2 +13,trimodal,24,TTest,0,50,1,3 +13,trimodal,24,LogReg,0,50,0,50 +13,trimodal,24,recursieve,0,50,0,50 +14,bimodal,0,Wilcoxon,5,49,0,3 +14,bimodal,0,TTest,1,50,0,4 +14,bimodal,0,LogReg,0,50,0,50 +14,bimodal,0,recursieve,10,48,10,48 +14,bimodal,1,Wilcoxon,5,49,0,3 +14,bimodal,1,TTest,1,50,0,4 +14,bimodal,1,LogReg,0,50,0,50 +14,bimodal,1,recursieve,8,37,8,37 +14,bimodal,2,Wilcoxon,5,49,0,3 +14,bimodal,2,TTest,1,50,0,4 +14,bimodal,2,LogReg,0,50,0,50 +14,bimodal,2,recursieve,10,41,10,41 +14,bimodal,3,Wilcoxon,5,49,0,3 +14,bimodal,3,TTest,1,50,0,4 +14,bimodal,3,LogReg,0,50,0,50 +14,bimodal,3,recursieve,10,48,10,48 +14,bimodal,4,Wilcoxon,5,49,0,3 +14,bimodal,4,TTest,1,50,0,4 +14,bimodal,4,LogReg,0,50,0,50 +14,bimodal,4,recursieve,10,39,10,39 +14,bimodal,5,Wilcoxon,5,49,0,3 +14,bimodal,5,TTest,1,50,0,4 +14,bimodal,5,LogReg,0,50,0,50 +14,bimodal,5,recursieve,10,30,10,30 +14,bimodal,6,Wilcoxon,5,49,0,3 +14,bimodal,6,TTest,1,50,0,4 +14,bimodal,6,LogReg,0,50,0,50 +14,bimodal,6,recursieve,10,50,10,50 +14,bimodal,7,Wilcoxon,5,49,0,3 +14,bimodal,7,TTest,1,50,0,4 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+14,null,7,TTest,0,50,0,0 +14,null,7,LogReg,0,50,0,50 +14,null,7,recursieve,0,50,0,50 +14,null,8,Wilcoxon,0,29,0,0 +14,null,8,TTest,0,50,0,0 +14,null,8,LogReg,0,50,0,50 +14,null,8,recursieve,0,47,0,47 +14,null,9,Wilcoxon,0,29,0,0 +14,null,9,TTest,0,50,0,0 +14,null,9,LogReg,0,50,0,50 +14,null,9,recursieve,0,23,0,23 +14,null,10,Wilcoxon,0,29,0,0 +14,null,10,TTest,0,50,0,0 +14,null,10,LogReg,0,50,0,50 +14,null,10,recursieve,0,50,0,50 +14,null,11,Wilcoxon,0,29,0,0 +14,null,11,TTest,0,50,0,0 +14,null,11,LogReg,0,50,0,50 +14,null,11,recursieve,0,27,0,27 +14,null,12,Wilcoxon,0,29,0,0 +14,null,12,TTest,0,50,0,0 +14,null,12,LogReg,0,50,0,50 +14,null,12,recursieve,0,27,0,27 +14,null,13,Wilcoxon,0,29,0,0 +14,null,13,TTest,0,50,0,0 +14,null,13,LogReg,0,50,0,50 +14,null,13,recursieve,0,50,0,50 +14,null,14,Wilcoxon,0,29,0,0 +14,null,14,TTest,0,50,0,0 +14,null,14,LogReg,0,50,0,50 +14,null,14,recursieve,0,43,0,43 +14,null,15,Wilcoxon,0,29,0,0 +14,null,15,TTest,0,50,0,0 +14,null,15,LogReg,0,50,0,50 +14,null,15,recursieve,0,16,0,16 +14,null,16,Wilcoxon,0,29,0,0 +14,null,16,TTest,0,50,0,0 +14,null,16,LogReg,0,50,0,50 +14,null,16,recursieve,0,50,0,50 +14,null,17,Wilcoxon,0,29,0,0 +14,null,17,TTest,0,50,0,0 +14,null,17,LogReg,0,50,0,50 +14,null,17,recursieve,0,32,0,32 +14,null,18,Wilcoxon,0,29,0,0 +14,null,18,TTest,0,50,0,0 +14,null,18,LogReg,0,50,0,50 +14,null,18,recursieve,0,34,0,34 +14,null,19,Wilcoxon,0,29,0,0 +14,null,19,TTest,0,50,0,0 +14,null,19,LogReg,0,50,0,50 +14,null,19,recursieve,0,45,0,45 +14,null,20,Wilcoxon,0,29,0,0 +14,null,20,TTest,0,50,0,0 +14,null,20,LogReg,0,50,0,50 +14,null,20,recursieve,0,34,0,34 +14,null,21,Wilcoxon,0,29,0,0 +14,null,21,TTest,0,50,0,0 +14,null,21,LogReg,0,50,0,50 +14,null,21,recursieve,0,13,0,13 +14,null,22,Wilcoxon,0,29,0,0 +14,null,22,TTest,0,50,0,0 +14,null,22,LogReg,0,50,0,50 +14,null,22,recursieve,0,33,0,33 +14,null,23,Wilcoxon,0,29,0,0 +14,null,23,TTest,0,50,0,0 +14,null,23,LogReg,0,50,0,50 +14,null,23,recursieve,0,40,0,40 +14,null,24,Wilcoxon,0,29,0,0 +14,null,24,TTest,0,50,0,0 +14,null,24,LogReg,0,50,0,50 +14,null,24,recursieve,0,27,0,27 +14,skew,0,Wilcoxon,2,46,0,3 +14,skew,0,TTest,1,50,0,4 +14,skew,0,LogReg,1,50,1,50 +14,skew,0,recursieve,10,50,10,50 +14,skew,1,Wilcoxon,2,46,0,3 +14,skew,1,TTest,1,50,0,4 +14,skew,1,LogReg,1,50,1,50 +14,skew,1,recursieve,10,35,10,35 +14,skew,2,Wilcoxon,2,46,0,3 +14,skew,2,TTest,1,50,0,4 +14,skew,2,LogReg,1,50,1,50 +14,skew,2,recursieve,10,50,10,50 +14,skew,3,Wilcoxon,2,46,0,3 +14,skew,3,TTest,1,50,0,4 +14,skew,3,LogReg,1,50,1,50 +14,skew,3,recursieve,10,50,10,50 +14,skew,4,Wilcoxon,2,46,0,3 +14,skew,4,TTest,1,50,0,4 +14,skew,4,LogReg,1,50,1,50 +14,skew,4,recursieve,10,25,10,25 +14,skew,5,Wilcoxon,2,46,0,3 +14,skew,5,TTest,1,50,0,4 +14,skew,5,LogReg,1,50,1,50 +14,skew,5,recursieve,10,50,10,50 +14,skew,6,Wilcoxon,2,46,0,3 +14,skew,6,TTest,1,50,0,4 +14,skew,6,LogReg,1,50,1,50 +14,skew,6,recursieve,10,50,10,50 +14,skew,7,Wilcoxon,2,46,0,3 +14,skew,7,TTest,1,50,0,4 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+18,null,7,recursieve,0,50,0,50 +18,null,8,Wilcoxon,0,23,0,0 +18,null,8,TTest,0,50,0,0 +18,null,8,LogReg,0,50,0,50 +18,null,8,recursieve,0,37,0,37 +18,null,9,Wilcoxon,0,23,0,0 +18,null,9,TTest,0,50,0,0 +18,null,9,LogReg,0,50,0,50 +18,null,9,recursieve,0,34,0,34 +18,null,10,Wilcoxon,0,23,0,0 +18,null,10,TTest,0,50,0,0 +18,null,10,LogReg,0,50,0,50 +18,null,10,recursieve,0,31,0,31 +18,null,11,Wilcoxon,0,23,0,0 +18,null,11,TTest,0,50,0,0 +18,null,11,LogReg,0,50,0,50 +18,null,11,recursieve,0,50,0,50 +18,null,12,Wilcoxon,0,23,0,0 +18,null,12,TTest,0,50,0,0 +18,null,12,LogReg,0,50,0,50 +18,null,12,recursieve,0,44,0,44 +18,null,13,Wilcoxon,0,23,0,0 +18,null,13,TTest,0,50,0,0 +18,null,13,LogReg,0,50,0,50 +18,null,13,recursieve,0,46,0,46 +18,null,14,Wilcoxon,0,23,0,0 +18,null,14,TTest,0,50,0,0 +18,null,14,LogReg,0,50,0,50 +18,null,14,recursieve,0,41,0,41 +18,null,15,Wilcoxon,0,23,0,0 +18,null,15,TTest,0,50,0,0 +18,null,15,LogReg,0,50,0,50 +18,null,15,recursieve,0,44,0,44 +18,null,16,Wilcoxon,0,23,0,0 +18,null,16,TTest,0,50,0,0 +18,null,16,LogReg,0,50,0,50 +18,null,16,recursieve,0,14,0,14 +18,null,17,Wilcoxon,0,23,0,0 +18,null,17,TTest,0,50,0,0 +18,null,17,LogReg,0,50,0,50 +18,null,17,recursieve,0,38,0,38 +18,null,18,Wilcoxon,0,23,0,0 +18,null,18,TTest,0,50,0,0 +18,null,18,LogReg,0,50,0,50 +18,null,18,recursieve,0,50,0,50 +18,null,19,Wilcoxon,0,23,0,0 +18,null,19,TTest,0,50,0,0 +18,null,19,LogReg,0,50,0,50 +18,null,19,recursieve,0,50,0,50 +18,null,20,Wilcoxon,0,23,0,0 +18,null,20,TTest,0,50,0,0 +18,null,20,LogReg,0,50,0,50 +18,null,20,recursieve,0,50,0,50 +18,null,21,Wilcoxon,0,23,0,0 +18,null,21,TTest,0,50,0,0 +18,null,21,LogReg,0,50,0,50 +18,null,21,recursieve,0,50,0,50 +18,null,22,Wilcoxon,0,23,0,0 +18,null,22,TTest,0,50,0,0 +18,null,22,LogReg,0,50,0,50 +18,null,22,recursieve,0,50,0,50 +18,null,23,Wilcoxon,0,23,0,0 +18,null,23,TTest,0,50,0,0 +18,null,23,LogReg,0,50,0,50 +18,null,23,recursieve,0,39,0,39 +18,null,24,Wilcoxon,0,23,0,0 +18,null,24,TTest,0,50,0,0 +18,null,24,LogReg,0,50,0,50 +18,null,24,recursieve,0,38,0,38 +18,skew,0,Wilcoxon,0,38,0,1 +18,skew,0,TTest,0,50,0,1 +18,skew,0,LogReg,0,50,0,50 +18,skew,0,recursieve,5,32,5,32 +18,skew,1,Wilcoxon,0,38,0,1 +18,skew,1,TTest,0,50,0,1 +18,skew,1,LogReg,0,50,0,50 +18,skew,1,recursieve,5,33,5,33 +18,skew,2,Wilcoxon,0,38,0,1 +18,skew,2,TTest,0,50,0,1 +18,skew,2,LogReg,0,50,0,50 +18,skew,2,recursieve,10,30,10,30 +18,skew,3,Wilcoxon,0,38,0,1 +18,skew,3,TTest,0,50,0,1 +18,skew,3,LogReg,0,50,0,50 +18,skew,3,recursieve,0,46,0,46 +18,skew,4,Wilcoxon,0,38,0,1 +18,skew,4,TTest,0,50,0,1 +18,skew,4,LogReg,0,50,0,50 +18,skew,4,recursieve,9,50,9,50 +18,skew,5,Wilcoxon,0,38,0,1 +18,skew,5,TTest,0,50,0,1 +18,skew,5,LogReg,0,50,0,50 +18,skew,5,recursieve,1,24,1,24 +18,skew,6,Wilcoxon,0,38,0,1 +18,skew,6,TTest,0,50,0,1 +18,skew,6,LogReg,0,50,0,50 +18,skew,6,recursieve,1,50,1,50 +18,skew,7,Wilcoxon,0,38,0,1 +18,skew,7,TTest,0,50,0,1 +18,skew,7,LogReg,0,50,0,50 +18,skew,7,recursieve,9,23,9,23 +18,skew,8,Wilcoxon,0,38,0,1 +18,skew,8,TTest,0,50,0,1 +18,skew,8,LogReg,0,50,0,50 +18,skew,8,recursieve,4,37,4,37 +18,skew,9,Wilcoxon,0,38,0,1 +18,skew,9,TTest,0,50,0,1 +18,skew,9,LogReg,0,50,0,50 +18,skew,9,recursieve,1,30,1,30 +18,skew,10,Wilcoxon,0,38,0,1 +18,skew,10,TTest,0,50,0,1 +18,skew,10,LogReg,0,50,0,50 +18,skew,10,recursieve,9,38,9,38 +18,skew,11,Wilcoxon,0,38,0,1 +18,skew,11,TTest,0,50,0,1 +18,skew,11,LogReg,0,50,0,50 +18,skew,11,recursieve,10,49,10,49 +18,skew,12,Wilcoxon,0,38,0,1 +18,skew,12,TTest,0,50,0,1 +18,skew,12,LogReg,0,50,0,50 +18,skew,12,recursieve,10,50,10,50 +18,skew,13,Wilcoxon,0,38,0,1 +18,skew,13,TTest,0,50,0,1 +18,skew,13,LogReg,0,50,0,50 +18,skew,13,recursieve,10,50,10,50 +18,skew,14,Wilcoxon,0,38,0,1 +18,skew,14,TTest,0,50,0,1 +18,skew,14,LogReg,0,50,0,50 +18,skew,14,recursieve,10,30,10,30 +18,skew,15,Wilcoxon,0,38,0,1 +18,skew,15,TTest,0,50,0,1 +18,skew,15,LogReg,0,50,0,50 +18,skew,15,recursieve,10,32,10,32 +18,skew,16,Wilcoxon,0,38,0,1 +18,skew,16,TTest,0,50,0,1 +18,skew,16,LogReg,0,50,0,50 +18,skew,16,recursieve,10,50,10,50 +18,skew,17,Wilcoxon,0,38,0,1 +18,skew,17,TTest,0,50,0,1 +18,skew,17,LogReg,0,50,0,50 +18,skew,17,recursieve,5,32,5,32 +18,skew,18,Wilcoxon,0,38,0,1 +18,skew,18,TTest,0,50,0,1 +18,skew,18,LogReg,0,50,0,50 +18,skew,18,recursieve,9,28,9,28 +18,skew,19,Wilcoxon,0,38,0,1 +18,skew,19,TTest,0,50,0,1 +18,skew,19,LogReg,0,50,0,50 +18,skew,19,recursieve,10,22,10,22 +18,skew,20,Wilcoxon,0,38,0,1 +18,skew,20,TTest,0,50,0,1 +18,skew,20,LogReg,0,50,0,50 +18,skew,20,recursieve,7,41,7,41 +18,skew,21,Wilcoxon,0,38,0,1 +18,skew,21,TTest,0,50,0,1 +18,skew,21,LogReg,0,50,0,50 +18,skew,21,recursieve,10,50,10,50 +18,skew,22,Wilcoxon,0,38,0,1 +18,skew,22,TTest,0,50,0,1 +18,skew,22,LogReg,0,50,0,50 +18,skew,22,recursieve,5,31,5,31 +18,skew,23,Wilcoxon,0,38,0,1 +18,skew,23,TTest,0,50,0,1 +18,skew,23,LogReg,0,50,0,50 +18,skew,23,recursieve,4,23,4,23 +18,skew,24,Wilcoxon,0,38,0,1 +18,skew,24,TTest,0,50,0,1 +18,skew,24,LogReg,0,50,0,50 +18,skew,24,recursieve,1,36,1,36 +18,trimodal,0,Wilcoxon,1,39,0,1 +18,trimodal,0,TTest,0,50,0,1 +18,trimodal,0,LogReg,0,50,0,50 +18,trimodal,0,recursieve,3,34,3,34 +18,trimodal,1,Wilcoxon,1,39,0,1 +18,trimodal,1,TTest,0,50,0,1 +18,trimodal,1,LogReg,0,50,0,50 +18,trimodal,1,recursieve,0,21,0,21 +18,trimodal,2,Wilcoxon,1,39,0,1 +18,trimodal,2,TTest,0,50,0,1 +18,trimodal,2,LogReg,0,50,0,50 +18,trimodal,2,recursieve,1,50,1,50 +18,trimodal,3,Wilcoxon,1,39,0,1 +18,trimodal,3,TTest,0,50,0,1 +18,trimodal,3,LogReg,0,50,0,50 +18,trimodal,3,recursieve,3,50,3,50 +18,trimodal,4,Wilcoxon,1,39,0,1 +18,trimodal,4,TTest,0,50,0,1 +18,trimodal,4,LogReg,0,50,0,50 +18,trimodal,4,recursieve,1,40,1,40 +18,trimodal,5,Wilcoxon,1,39,0,1 +18,trimodal,5,TTest,0,50,0,1 +18,trimodal,5,LogReg,0,50,0,50 +18,trimodal,5,recursieve,6,43,6,43 +18,trimodal,6,Wilcoxon,1,39,0,1 +18,trimodal,6,TTest,0,50,0,1 +18,trimodal,6,LogReg,0,50,0,50 +18,trimodal,6,recursieve,1,50,1,50 +18,trimodal,7,Wilcoxon,1,39,0,1 +18,trimodal,7,TTest,0,50,0,1 +18,trimodal,7,LogReg,0,50,0,50 +18,trimodal,7,recursieve,3,27,3,27 +18,trimodal,8,Wilcoxon,1,39,0,1 +18,trimodal,8,TTest,0,50,0,1 +18,trimodal,8,LogReg,0,50,0,50 +18,trimodal,8,recursieve,0,27,0,27 +18,trimodal,9,Wilcoxon,1,39,0,1 +18,trimodal,9,TTest,0,50,0,1 +18,trimodal,9,LogReg,0,50,0,50 +18,trimodal,9,recursieve,8,46,8,46 +18,trimodal,10,Wilcoxon,1,39,0,1 +18,trimodal,10,TTest,0,50,0,1 +18,trimodal,10,LogReg,0,50,0,50 +18,trimodal,10,recursieve,0,28,0,28 +18,trimodal,11,Wilcoxon,1,39,0,1 +18,trimodal,11,TTest,0,50,0,1 +18,trimodal,11,LogReg,0,50,0,50 +18,trimodal,11,recursieve,5,45,5,45 +18,trimodal,12,Wilcoxon,1,39,0,1 +18,trimodal,12,TTest,0,50,0,1 +18,trimodal,12,LogReg,0,50,0,50 +18,trimodal,12,recursieve,4,46,4,46 +18,trimodal,13,Wilcoxon,1,39,0,1 +18,trimodal,13,TTest,0,50,0,1 +18,trimodal,13,LogReg,0,50,0,50 +18,trimodal,13,recursieve,2,36,2,36 +18,trimodal,14,Wilcoxon,1,39,0,1 +18,trimodal,14,TTest,0,50,0,1 +18,trimodal,14,LogReg,0,50,0,50 +18,trimodal,14,recursieve,0,44,0,44 +18,trimodal,15,Wilcoxon,1,39,0,1 +18,trimodal,15,TTest,0,50,0,1 +18,trimodal,15,LogReg,0,50,0,50 +18,trimodal,15,recursieve,0,38,0,38 +18,trimodal,16,Wilcoxon,1,39,0,1 +18,trimodal,16,TTest,0,50,0,1 +18,trimodal,16,LogReg,0,50,0,50 +18,trimodal,16,recursieve,5,38,5,38 +18,trimodal,17,Wilcoxon,1,39,0,1 +18,trimodal,17,TTest,0,50,0,1 +18,trimodal,17,LogReg,0,50,0,50 +18,trimodal,17,recursieve,2,43,2,43 +18,trimodal,18,Wilcoxon,1,39,0,1 +18,trimodal,18,TTest,0,50,0,1 +18,trimodal,18,LogReg,0,50,0,50 +18,trimodal,18,recursieve,5,50,5,50 +18,trimodal,19,Wilcoxon,1,39,0,1 +18,trimodal,19,TTest,0,50,0,1 +18,trimodal,19,LogReg,0,50,0,50 +18,trimodal,19,recursieve,6,30,6,30 +18,trimodal,20,Wilcoxon,1,39,0,1 +18,trimodal,20,TTest,0,50,0,1 +18,trimodal,20,LogReg,0,50,0,50 +18,trimodal,20,recursieve,0,18,0,18 +18,trimodal,21,Wilcoxon,1,39,0,1 +18,trimodal,21,TTest,0,50,0,1 +18,trimodal,21,LogReg,0,50,0,50 +18,trimodal,21,recursieve,2,44,2,44 +18,trimodal,22,Wilcoxon,1,39,0,1 +18,trimodal,22,TTest,0,50,0,1 +18,trimodal,22,LogReg,0,50,0,50 +18,trimodal,22,recursieve,0,16,0,16 +18,trimodal,23,Wilcoxon,1,39,0,1 +18,trimodal,23,TTest,0,50,0,1 +18,trimodal,23,LogReg,0,50,0,50 +18,trimodal,23,recursieve,1,50,1,50 +18,trimodal,24,Wilcoxon,1,39,0,1 +18,trimodal,24,TTest,0,50,0,1 +18,trimodal,24,LogReg,0,50,0,50 +18,trimodal,24,recursieve,2,39,2,39 +19,bimodal,0,Wilcoxon,1,31,0,2 +19,bimodal,0,TTest,2,50,0,3 +19,bimodal,0,LogReg,1,50,1,50 +19,bimodal,0,recursieve,0,50,0,50 +19,bimodal,1,Wilcoxon,1,31,0,2 +19,bimodal,1,TTest,2,50,0,3 +19,bimodal,1,LogReg,1,50,1,50 +19,bimodal,1,recursieve,0,48,0,48 +19,bimodal,2,Wilcoxon,1,31,0,2 +19,bimodal,2,TTest,2,50,0,3 +19,bimodal,2,LogReg,1,50,1,50 +19,bimodal,2,recursieve,0,45,0,45 +19,bimodal,3,Wilcoxon,1,31,0,2 +19,bimodal,3,TTest,2,50,0,3 +19,bimodal,3,LogReg,1,50,1,50 +19,bimodal,3,recursieve,0,50,0,50 +19,bimodal,4,Wilcoxon,1,31,0,2 +19,bimodal,4,TTest,2,50,0,3 +19,bimodal,4,LogReg,1,50,1,50 +19,bimodal,4,recursieve,0,31,0,31 +19,bimodal,5,Wilcoxon,1,31,0,2 +19,bimodal,5,TTest,2,50,0,3 +19,bimodal,5,LogReg,1,50,1,50 +19,bimodal,5,recursieve,0,13,0,13 +19,bimodal,6,Wilcoxon,1,31,0,2 +19,bimodal,6,TTest,2,50,0,3 +19,bimodal,6,LogReg,1,50,1,50 +19,bimodal,6,recursieve,0,45,0,45 +19,bimodal,7,Wilcoxon,1,31,0,2 +19,bimodal,7,TTest,2,50,0,3 +19,bimodal,7,LogReg,1,50,1,50 +19,bimodal,7,recursieve,0,12,0,12 +19,bimodal,8,Wilcoxon,1,31,0,2 +19,bimodal,8,TTest,2,50,0,3 +19,bimodal,8,LogReg,1,50,1,50 +19,bimodal,8,recursieve,0,33,0,33 +19,bimodal,9,Wilcoxon,1,31,0,2 +19,bimodal,9,TTest,2,50,0,3 +19,bimodal,9,LogReg,1,50,1,50 +19,bimodal,9,recursieve,0,37,0,37 +19,bimodal,10,Wilcoxon,1,31,0,2 +19,bimodal,10,TTest,2,50,0,3 +19,bimodal,10,LogReg,1,50,1,50 +19,bimodal,10,recursieve,0,50,0,50 +19,bimodal,11,Wilcoxon,1,31,0,2 +19,bimodal,11,TTest,2,50,0,3 +19,bimodal,11,LogReg,1,50,1,50 +19,bimodal,11,recursieve,0,50,0,50 +19,bimodal,12,Wilcoxon,1,31,0,2 +19,bimodal,12,TTest,2,50,0,3 +19,bimodal,12,LogReg,1,50,1,50 +19,bimodal,12,recursieve,0,50,0,50 +19,bimodal,13,Wilcoxon,1,31,0,2 +19,bimodal,13,TTest,2,50,0,3 +19,bimodal,13,LogReg,1,50,1,50 +19,bimodal,13,recursieve,1,47,1,47 +19,bimodal,14,Wilcoxon,1,31,0,2 +19,bimodal,14,TTest,2,50,0,3 +19,bimodal,14,LogReg,1,50,1,50 +19,bimodal,14,recursieve,0,26,0,26 +19,bimodal,15,Wilcoxon,1,31,0,2 +19,bimodal,15,TTest,2,50,0,3 +19,bimodal,15,LogReg,1,50,1,50 +19,bimodal,15,recursieve,0,47,0,47 +19,bimodal,16,Wilcoxon,1,31,0,2 +19,bimodal,16,TTest,2,50,0,3 +19,bimodal,16,LogReg,1,50,1,50 +19,bimodal,16,recursieve,0,30,0,30 +19,bimodal,17,Wilcoxon,1,31,0,2 +19,bimodal,17,TTest,2,50,0,3 +19,bimodal,17,LogReg,1,50,1,50 +19,bimodal,17,recursieve,0,18,0,18 +19,bimodal,18,Wilcoxon,1,31,0,2 +19,bimodal,18,TTest,2,50,0,3 +19,bimodal,18,LogReg,1,50,1,50 +19,bimodal,18,recursieve,0,44,0,44 +19,bimodal,19,Wilcoxon,1,31,0,2 +19,bimodal,19,TTest,2,50,0,3 +19,bimodal,19,LogReg,1,50,1,50 +19,bimodal,19,recursieve,0,41,0,41 +19,bimodal,20,Wilcoxon,1,31,0,2 +19,bimodal,20,TTest,2,50,0,3 +19,bimodal,20,LogReg,1,50,1,50 +19,bimodal,20,recursieve,0,50,0,50 +19,bimodal,21,Wilcoxon,1,31,0,2 +19,bimodal,21,TTest,2,50,0,3 +19,bimodal,21,LogReg,1,50,1,50 +19,bimodal,21,recursieve,1,38,1,38 +19,bimodal,22,Wilcoxon,1,31,0,2 +19,bimodal,22,TTest,2,50,0,3 +19,bimodal,22,LogReg,1,50,1,50 +19,bimodal,22,recursieve,0,50,0,50 +19,bimodal,23,Wilcoxon,1,31,0,2 +19,bimodal,23,TTest,2,50,0,3 +19,bimodal,23,LogReg,1,50,1,50 +19,bimodal,23,recursieve,1,34,1,34 +19,bimodal,24,Wilcoxon,1,31,0,2 +19,bimodal,24,TTest,2,50,0,3 +19,bimodal,24,LogReg,1,50,1,50 +19,bimodal,24,recursieve,0,44,0,44 +19,coexpression,0,Wilcoxon,1,31,0,2 +19,coexpression,0,TTest,1,50,0,3 +19,coexpression,0,LogReg,3,50,3,50 +19,coexpression,0,recursieve,0,43,0,43 +19,coexpression,1,Wilcoxon,1,31,0,2 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+19,null,3,TTest,0,50,0,0 +19,null,3,LogReg,0,50,0,50 +19,null,3,recursieve,0,43,0,43 +19,null,4,Wilcoxon,0,19,0,0 +19,null,4,TTest,0,50,0,0 +19,null,4,LogReg,0,50,0,50 +19,null,4,recursieve,0,31,0,31 +19,null,5,Wilcoxon,0,19,0,0 +19,null,5,TTest,0,50,0,0 +19,null,5,LogReg,0,50,0,50 +19,null,5,recursieve,0,25,0,25 +19,null,6,Wilcoxon,0,19,0,0 +19,null,6,TTest,0,50,0,0 +19,null,6,LogReg,0,50,0,50 +19,null,6,recursieve,0,43,0,43 +19,null,7,Wilcoxon,0,19,0,0 +19,null,7,TTest,0,50,0,0 +19,null,7,LogReg,0,50,0,50 +19,null,7,recursieve,0,35,0,35 +19,null,8,Wilcoxon,0,19,0,0 +19,null,8,TTest,0,50,0,0 +19,null,8,LogReg,0,50,0,50 +19,null,8,recursieve,0,39,0,39 +19,null,9,Wilcoxon,0,19,0,0 +19,null,9,TTest,0,50,0,0 +19,null,9,LogReg,0,50,0,50 +19,null,9,recursieve,0,50,0,50 +19,null,10,Wilcoxon,0,19,0,0 +19,null,10,TTest,0,50,0,0 +19,null,10,LogReg,0,50,0,50 +19,null,10,recursieve,0,16,0,16 +19,null,11,Wilcoxon,0,19,0,0 +19,null,11,TTest,0,50,0,0 +19,null,11,LogReg,0,50,0,50 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ziJ&cZoM)cRo_!VE)DO--<^kmma(*&;$sl{2sKx_HKjG0NOkH_EyvUiRh;j7i&2bAu zAL(Ms6{I4i5Oy{3^ECRtefb?^6jC~vBRqt}RJh&NKxy(m?AGd)4#alzvVCkEpA~Nz zU;sXD%bEIqAJ0lfQKU4pVjxda!ubGJB3c@E9uo9T{I$n!wtfg* z{tyY1J2^!^{=^M*d!8+_$Kwa*`93Q5&n(%gtGJoH)NW(B$tWWu2ab>tg^aquA=E16 z7PG$89yLwFj&F-883)cpKPy@&lg!=deU)?|{6Vt>xPlzxca&D$qFomyAfG!Ag?zY1 zLqvLGBYK<=%R>*W74M|*53^WcqTLMU*M`c0w~+@>Qzh2!^k0l*8$7t1l^rM z-lJ%LrGk2OtAtmh?Go3PRf9H9qd!N21ChIJcngV%4_3TON-5{*Yr_^2M^SZ|)Ocl0 z0toHUi(z|R@e~a6zJ!<|bW$M<5`9`e{s`}dh#c{&FGjgbj`i^UZaZosFI&`oE}2%JF$O9h;XPu)VgeU($){zcal2z9AjL^OZ%H=c}x_#4{B fA^abqW$ho;PjN5N+LhPIahq*vYjJGGqV@j;&#k^t diff --git a/manuscript/robustness/microglia/alzheimers_coexpression_null_gene_frequency.csv b/manuscript/robustness/microglia/alzheimers_coexpression_null_gene_frequency.csv index 6dbef7e..2a8a5c2 100644 --- a/manuscript/robustness/microglia/alzheimers_coexpression_null_gene_frequency.csv +++ b/manuscript/robustness/microglia/alzheimers_coexpression_null_gene_frequency.csv @@ -1,21 +1,21 @@ -gene,rank,n_shuffled_panels,shuffled_frequency,p_value -SAT1,1,1,0.04,0.07692307692307693 -ST6GALNAC3,2,0,0.0,0.038461538461538464 -LINC00486,3,1,0.04,0.07692307692307693 -NEAT1,4,0,0.0,0.038461538461538464 -MEF2C,5,0,0.0,0.038461538461538464 -ST6GAL1,6,1,0.04,0.07692307692307693 -LINC02798,7,0,0.0,0.038461538461538464 -LUC7L3,8,0,0.0,0.038461538461538464 -C3,9,0,0.0,0.038461538461538464 -NAV3,10,2,0.08,0.11538461538461539 -RBM25,11,0,0.0,0.038461538461538464 -ATRX,12,0,0.0,0.038461538461538464 -PNISR,13,0,0.0,0.038461538461538464 -LNCAROD,14,0,0.0,0.038461538461538464 -SDK1,15,1,0.04,0.07692307692307693 -HSP90AA1,16,0,0.0,0.038461538461538464 -ENSG00000285534,17,1,0.04,0.07692307692307693 -SYNDIG1,18,1,0.04,0.07692307692307693 -PRPF38B,19,1,0.04,0.07692307692307693 -CD74,20,1,0.04,0.07692307692307693 +gene,rank,n_shuffled_panels,shuffled_frequency,p_value,p_adj +SAT1,1,2,0.02,0.0297029702970297,0.034944670937682006 +ST6GALNAC3,2,2,0.02,0.0297029702970297,0.034944670937682006 +LINC00486,3,1,0.01,0.019801980198019802,0.03300330033003301 +NEAT1,4,1,0.01,0.019801980198019802,0.03300330033003301 +MEF2C,5,1,0.01,0.019801980198019802,0.03300330033003301 +ST6GAL1,6,2,0.02,0.0297029702970297,0.034944670937682006 +LINC02798,7,1,0.01,0.019801980198019802,0.03300330033003301 +LUC7L3,8,0,0.0,0.009900990099009901,0.03300330033003301 +C3,9,0,0.0,0.009900990099009901,0.03300330033003301 +NAV3,10,4,0.04,0.04950495049504951,0.04950495049504951 +RBM25,11,0,0.0,0.009900990099009901,0.03300330033003301 +ATRX,12,0,0.0,0.009900990099009901,0.03300330033003301 +PNISR,13,0,0.0,0.009900990099009901,0.03300330033003301 +LNCAROD,14,1,0.01,0.019801980198019802,0.03300330033003301 +SDK1,15,1,0.01,0.019801980198019802,0.03300330033003301 +HSP90AA1,16,0,0.0,0.009900990099009901,0.03300330033003301 +ENSG00000285534,17,4,0.04,0.04950495049504951,0.04950495049504951 +SYNDIG1,18,2,0.02,0.0297029702970297,0.034944670937682006 +PRPF38B,19,2,0.02,0.0297029702970297,0.034944670937682006 +CD74,20,4,0.04,0.04950495049504951,0.04950495049504951 diff --git a/manuscript/robustness/microglia/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/microglia/alzheimers_coexpression_null_panels.csv index a17fa49..cfb3aee 100644 --- a/manuscript/robustness/microglia/alzheimers_coexpression_null_panels.csv +++ b/manuscript/robustness/microglia/alzheimers_coexpression_null_panels.csv @@ -519,3 +519,1503 @@ shuffle_25,17,SLC35C1 shuffle_25,18,RGSL1 shuffle_25,19,FAM133DP shuffle_25,20,ENSG00000248774 +shuffle_26,1,MROH7-TTC4 +shuffle_26,2,GPR158 +shuffle_26,3,FMN2 +shuffle_26,4,TMSB4X +shuffle_26,5,ENSG00000276128 +shuffle_26,6,PRP4K +shuffle_26,7,ZDHHC11B +shuffle_26,8,CDH17 +shuffle_26,9,BLOC1S6 +shuffle_26,10,ENSG00000270792 +shuffle_26,11,GIPC3 +shuffle_26,12,SERP2 +shuffle_26,13,KLF11 +shuffle_26,14,LINC01934 +shuffle_26,15,HTR2A +shuffle_26,16,HERC2P3 +shuffle_26,17,ESR2 +shuffle_26,18,TPRN +shuffle_26,19,ENSG00000283692 +shuffle_26,20,SELENOP +shuffle_27,1,SRRM2 +shuffle_27,2,CD74 +shuffle_27,3,SRGN +shuffle_27,4,SYT12 +shuffle_27,5,BIN2 +shuffle_27,6,ENSG00000286536 +shuffle_27,7,CNTN1 +shuffle_27,8,ENSG00000286215 +shuffle_27,9,OPALIN +shuffle_27,10,PPP1R15A +shuffle_27,11,LHX2 +shuffle_27,12,CACNA1A +shuffle_27,13,SLC35C1 +shuffle_27,14,NR2F1 +shuffle_27,15,LINC01122 +shuffle_27,16,AK5 +shuffle_27,17,SOX6 +shuffle_27,18,RTF1 +shuffle_27,19,ANK1 +shuffle_27,20,ROBO1 +shuffle_28,1,USE1 +shuffle_28,2,RASL11A +shuffle_28,3,LINC02223 +shuffle_28,4,GPC6 +shuffle_28,5,MCMDC2 +shuffle_28,6,ENSG00000229337 +shuffle_28,7,LINC00960 +shuffle_28,8,MIR137HG +shuffle_28,9,NOTCH4 +shuffle_28,10,TMEM44 +shuffle_28,11,CACNG3 +shuffle_28,12,LRRC7 +shuffle_28,13,ENSG00000232451 +shuffle_28,14,LINC02814 +shuffle_28,15,SLC4A10 +shuffle_28,16,PDZD11 +shuffle_28,17,C14orf132 +shuffle_28,18,SGIP1 +shuffle_28,19,SLC5A4-AS1 +shuffle_28,20,PZP +shuffle_29,1,OBI1-AS1 +shuffle_29,2,CD1D +shuffle_29,3,EBF4 +shuffle_29,4,PAPPA2 +shuffle_29,5,C2orf80 +shuffle_29,6,SRRM4 +shuffle_29,7,INPP4B +shuffle_29,8,ENSG00000226022 +shuffle_29,9,CHMP6 +shuffle_29,10,GALNT17 +shuffle_29,11,CALN1 +shuffle_29,12,GADD45GIP1 +shuffle_29,13,SH3GL2 +shuffle_29,14,CDC25C +shuffle_29,15,ENSG00000285080 +shuffle_29,16,LINC02284 +shuffle_29,17,F13A1 +shuffle_29,18,ATF4 +shuffle_29,19,CDH9 +shuffle_29,20,INKA1 +shuffle_30,1,ENSG00000285784 +shuffle_30,2,ENSG00000287452 +shuffle_30,3,ENSG00000217455 +shuffle_30,4,PCDH10 +shuffle_30,5,CCDC144BP +shuffle_30,6,FAM171A1 +shuffle_30,7,F2RL1 +shuffle_30,8,CSKMT +shuffle_30,9,GSDMB +shuffle_30,10,CIRBP +shuffle_30,11,CCDC148 +shuffle_30,12,ENSG00000276900 +shuffle_30,13,PRP4K +shuffle_30,14,ST6GALNAC3 +shuffle_30,15,ENSG00000238260 +shuffle_30,16,ENSG00000262801 +shuffle_30,17,IRAG1 +shuffle_30,18,ENSG00000286540 +shuffle_30,19,ENSG00000285253 +shuffle_30,20,ABLIM1 +shuffle_31,1,LINC00992 +shuffle_31,2,CTNNAL1 +shuffle_31,3,FUT9 +shuffle_31,4,ECI2 +shuffle_31,5,PLEKHG3 +shuffle_31,6,GRM5P1 +shuffle_31,7,PRR16 +shuffle_31,8,NELL2 +shuffle_31,9,PWRN1 +shuffle_31,10,CD244 +shuffle_31,11,CHAMP1 +shuffle_31,12,MYO3B +shuffle_31,13,LRRTM4 +shuffle_31,14,ENSG00000225689 +shuffle_31,15,SYT1 +shuffle_31,16,CALN1 +shuffle_31,17,VSTM2B-DT +shuffle_31,18,HPGDS +shuffle_31,19,ENSG00000267748 +shuffle_31,20,TMEM37 +shuffle_32,1,TLE1 +shuffle_32,2,PRDM11 +shuffle_32,3,XIRP2 +shuffle_32,4,MAPK10 +shuffle_32,5,LINC00882 +shuffle_32,6,PDE7B +shuffle_32,7,ENSG00000269242 +shuffle_32,8,SH2D4A +shuffle_32,9,SLC7A11 +shuffle_32,10,FRA10AC1 +shuffle_32,11,LRRC4B +shuffle_32,12,ROBO1 +shuffle_32,13,GOLGB1 +shuffle_32,14,PKNOX2 +shuffle_32,15,LDB2 +shuffle_32,16,NPY +shuffle_32,17,ENSG00000261313 +shuffle_32,18,FAM171A1 +shuffle_32,19,ACSS3 +shuffle_32,20,UNC79 +shuffle_33,1,ENSG00000286721 +shuffle_33,2,ZNF568 +shuffle_33,3,PPIP5K1 +shuffle_33,4,LINC01608 +shuffle_33,5,SRSF11 +shuffle_33,6,CFAP46 +shuffle_33,7,ENSG00000286523 +shuffle_33,8,TAFA5 +shuffle_33,9,CCDC144A +shuffle_33,10,TBC1D32 +shuffle_33,11,INTS4P1 +shuffle_33,12,GAD2 +shuffle_33,13,ANO4 +shuffle_33,14,LNCAROD +shuffle_33,15,TTC41P +shuffle_33,16,LINC00996 +shuffle_33,17,CCDC178 +shuffle_33,18,SOX6 +shuffle_33,19,CCDC144CP +shuffle_33,20,TMEM132C +shuffle_34,1,MYRIP +shuffle_34,2,CCNT1 +shuffle_34,3,CNTNAP5 +shuffle_34,4,TMEM144 +shuffle_34,5,MGLL +shuffle_34,6,ZNF208 +shuffle_34,7,DNAJC9 +shuffle_34,8,TMSB10 +shuffle_34,9,SLC2A3 +shuffle_34,10,CCDC88A +shuffle_34,11,ADGRL3-AS1 +shuffle_34,12,SYNJ2 +shuffle_34,13,ENSG00000237400 +shuffle_34,14,LINC02994 +shuffle_34,15,RFXANK +shuffle_34,16,TMF1 +shuffle_34,17,ENSG00000261786 +shuffle_34,18,ENSG00000232035 +shuffle_34,19,PRRC2C +shuffle_34,20,ST3GAL3 +shuffle_35,1,CDH6 +shuffle_35,2,CRIP2 +shuffle_35,3,HCN1 +shuffle_35,4,ATP10B +shuffle_35,5,ENSG00000261786 +shuffle_35,6,LINC01122 +shuffle_35,7,LINC01517 +shuffle_35,8,ENSG00000253363 +shuffle_35,9,ENSG00000287122 +shuffle_35,10,ENSG00000267740 +shuffle_35,11,ENSG00000287950 +shuffle_35,12,PPP2R2B +shuffle_35,13,FYN +shuffle_35,14,ENSG00000285534 +shuffle_35,15,GALNT15 +shuffle_35,16,ENSG00000253474 +shuffle_35,17,RIF1 +shuffle_35,18,RRP9 +shuffle_35,19,TPRG1 +shuffle_35,20,RFTN1P1 +shuffle_36,1,IQCA1 +shuffle_36,2,MED18 +shuffle_36,3,OCIAD2 +shuffle_36,4,TMEM178B +shuffle_36,5,STX5-DT +shuffle_36,6,ENSG00000260370 +shuffle_36,7,SSRP1 +shuffle_36,8,ARMC9 +shuffle_36,9,LRP2 +shuffle_36,10,KCNV1 +shuffle_36,11,GASK1B-AS1 +shuffle_36,12,CYP7B1 +shuffle_36,13,MYH11 +shuffle_36,14,HAMP +shuffle_36,15,ABLIM1 +shuffle_36,16,NALF1 +shuffle_36,17,SNTG2 +shuffle_36,18,PLCB1 +shuffle_36,19,COPG2 +shuffle_36,20,ENSG00000277611 +shuffle_37,1,ASCL2 +shuffle_37,2,NPAS3 +shuffle_37,3,PRKD1 +shuffle_37,4,METTL21EP +shuffle_37,5,GREB1 +shuffle_37,6,ENOX1 +shuffle_37,7,ENSG00000250697 +shuffle_37,8,ENSG00000233290 +shuffle_37,9,PALLD +shuffle_37,10,ENSG00000259536 +shuffle_37,11,CD74 +shuffle_37,12,ZRANB2-DT +shuffle_37,13,LINC01608 +shuffle_37,14,MAPK4 +shuffle_37,15,ENSG00000285784 +shuffle_37,16,MECOM +shuffle_37,17,ENSG00000255241 +shuffle_37,18,SCLT1 +shuffle_37,19,KNDC1 +shuffle_37,20,PCGF1 +shuffle_38,1,LHX2 +shuffle_38,2,IL3RA +shuffle_38,3,PTPRR +shuffle_38,4,SPOCK3 +shuffle_38,5,GIMAP7 +shuffle_38,6,CDH13 +shuffle_38,7,MACROD2 +shuffle_38,8,PHYH +shuffle_38,9,CNTNAP2 +shuffle_38,10,RABL2A +shuffle_38,11,LINC00960 +shuffle_38,12,CACNG7 +shuffle_38,13,ZNF691-DT +shuffle_38,14,BAIAP2L1 +shuffle_38,15,HSPA1A +shuffle_38,16,CCNT1 +shuffle_38,17,DNAI4 +shuffle_38,18,IDH3B +shuffle_38,19,CACNA1C +shuffle_38,20,SOX6 +shuffle_39,1,KIAA1328 +shuffle_39,2,FAM229B +shuffle_39,3,TAFA2 +shuffle_39,4,MYO7B +shuffle_39,5,CSPG5 +shuffle_39,6,TRPM3 +shuffle_39,7,ZNF480 +shuffle_39,8,ANKRD55 +shuffle_39,9,LINC01344 +shuffle_39,10,KIF18B +shuffle_39,11,KCNAB1 +shuffle_39,12,BET1 +shuffle_39,13,ZBTB7A +shuffle_39,14,ENSG00000272449 +shuffle_39,15,IL2RA +shuffle_39,16,MED28-DT +shuffle_39,17,ECI2 +shuffle_39,18,DISC1FP1 +shuffle_39,19,LINC00299 +shuffle_39,20,ENSG00000253363 +shuffle_40,1,LINC00871 +shuffle_40,2,DDIT4-AS1 +shuffle_40,3,ENSG00000287474 +shuffle_40,4,PITRM1 +shuffle_40,5,LHFPL3 +shuffle_40,6,RRAS2 +shuffle_40,7,TESPA1 +shuffle_40,8,SF1-DT +shuffle_40,9,MTRFR +shuffle_40,10,ENSG00000283108 +shuffle_40,11,ITPRID1 +shuffle_40,12,SYT12 +shuffle_40,13,CRISPLD2 +shuffle_40,14,ST6GAL1 +shuffle_40,15,SLC16A2 +shuffle_40,16,STPG2 +shuffle_40,17,CTNNAL1 +shuffle_40,18,LINC01252 +shuffle_40,19,LINC01414 +shuffle_40,20,FAM171A1 +shuffle_41,1,CD74 +shuffle_41,2,ENSG00000250771 +shuffle_41,3,CD5L +shuffle_41,4,IGFBP5 +shuffle_41,5,SLC5A4-AS1 +shuffle_41,6,ENSG00000286836 +shuffle_41,7,GABRB1 +shuffle_41,8,ENSG00000285940 +shuffle_41,9,APOOL +shuffle_41,10,MICOS10 +shuffle_41,11,TNR +shuffle_41,12,THSD7B +shuffle_41,13,CSPG5 +shuffle_41,14,AFF2 +shuffle_41,15,GALNT17 +shuffle_41,16,B4GALNT4 +shuffle_41,17,ENSG00000262801 +shuffle_41,18,ENSG00000254444 +shuffle_41,19,CHL1 +shuffle_41,20,PPP1R1B +shuffle_42,1,ADRB1 +shuffle_42,2,HAMP +shuffle_42,3,CDK14 +shuffle_42,4,CTNNAL1 +shuffle_42,5,ENSG00000253474 +shuffle_42,6,LINC01331 +shuffle_42,7,ILRUN-AS1 +shuffle_42,8,FSTL4 +shuffle_42,9,ENSG00000287855 +shuffle_42,10,HYDIN +shuffle_42,11,DNTTIP2 +shuffle_42,12,FAAH2 +shuffle_42,13,CLU +shuffle_42,14,ZRANB2-DT +shuffle_42,15,VMO1 +shuffle_42,16,HPGDS +shuffle_42,17,KALRN +shuffle_42,18,B4GAT1-DT +shuffle_42,19,MYO3B +shuffle_42,20,DCDC1 +shuffle_43,1,KCNH5 +shuffle_43,2,ENSG00000275807 +shuffle_43,3,COL4A1 +shuffle_43,4,ZDHHC11B +shuffle_43,5,ENSG00000238260 +shuffle_43,6,GIPC3 +shuffle_43,7,PALLD +shuffle_43,8,FAM171A1 +shuffle_43,9,ZNF230 +shuffle_43,10,C1orf56 +shuffle_43,11,AQP4-AS1 +shuffle_43,12,LINC02112 +shuffle_43,13,HS3ST2 +shuffle_43,14,GABRA2 +shuffle_43,15,ANKDD1A +shuffle_43,16,RRP9 +shuffle_43,17,ZNF804A +shuffle_43,18,ITPRID1 +shuffle_43,19,SYNE1 +shuffle_43,20,ENSG00000285835 +shuffle_44,1,ZNF704 +shuffle_44,2,TNF +shuffle_44,3,PAM +shuffle_44,4,ENSG00000287474 +shuffle_44,5,OXSM +shuffle_44,6,ZC3H12A +shuffle_44,7,ANKRD55 +shuffle_44,8,BHLHE41 +shuffle_44,9,RGS7BP +shuffle_44,10,CPVL-AS2 +shuffle_44,11,ZBBX +shuffle_44,12,ITIH2 +shuffle_44,13,SGO1-AS1 +shuffle_44,14,SHTN1 +shuffle_44,15,CACNA2D3 +shuffle_44,16,ENSG00000250137 +shuffle_44,17,SHISA6 +shuffle_44,18,MYO3B +shuffle_44,19,PKN2-AS1 +shuffle_44,20,ENSG00000249001 +shuffle_45,1,ENSG00000286721 +shuffle_45,2,ALCAM +shuffle_45,3,ENSG00000285955 +shuffle_45,4,CAMK2A +shuffle_45,5,NBPF20 +shuffle_45,6,C1orf56 +shuffle_45,7,ZNF540 +shuffle_45,8,LINC01229 +shuffle_45,9,LINC01205 +shuffle_45,10,LINC01630 +shuffle_45,11,ZC3H12B +shuffle_45,12,B3GALT1 +shuffle_45,13,ERBB4 +shuffle_45,14,TPRN +shuffle_45,15,ST6GALNAC3 +shuffle_45,16,C4orf50 +shuffle_45,17,AKAP9 +shuffle_45,18,ELN +shuffle_45,19,CAVIN1 +shuffle_45,20,GTSCR1 +shuffle_46,1,MYO16 +shuffle_46,2,FAM171B +shuffle_46,3,SBDS +shuffle_46,4,PSMD7-DT +shuffle_46,5,ENSG00000266947 +shuffle_46,6,DCLK1 +shuffle_46,7,DTX1 +shuffle_46,8,KIF5C +shuffle_46,9,AGBL1 +shuffle_46,10,ADGRG1 +shuffle_46,11,PTPRK +shuffle_46,12,RFPL2 +shuffle_46,13,FGF14 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+shuffle_99,5,ENSG00000255241 +shuffle_99,6,ENSG00000267748 +shuffle_99,7,PPM1E +shuffle_99,8,RXFP1 +shuffle_99,9,DTX1 +shuffle_99,10,SUSD4 +shuffle_99,11,CLSTN2 +shuffle_99,12,BEND6 +shuffle_99,13,PEBP1 +shuffle_99,14,ZNF568 +shuffle_99,15,GIPC3 +shuffle_99,16,LINC00871 +shuffle_99,17,PRICKLE1 +shuffle_99,18,DNM1 +shuffle_99,19,FYB2 +shuffle_99,20,PLCB1 +shuffle_100,1,LINC00504 +shuffle_100,2,NEXMIF +shuffle_100,3,B3GALT1 +shuffle_100,4,ZNF804B +shuffle_100,5,ENSG00000287231 +shuffle_100,6,FAAH2 +shuffle_100,7,LARP7 +shuffle_100,8,OLFM3 +shuffle_100,9,SRP14 +shuffle_100,10,SYN2 +shuffle_100,11,GPX1 +shuffle_100,12,PRKG1 +shuffle_100,13,C14orf178 +shuffle_100,14,SPOCK1 +shuffle_100,15,LINC01182 +shuffle_100,16,LINC01500 +shuffle_100,17,BBOF1 +shuffle_100,18,ENSG00000237480 +shuffle_100,19,LARP1B +shuffle_100,20,AP2A2 diff --git a/manuscript/robustness/microglia/alzheimers_coexpression_null_pvalues.csv b/manuscript/robustness/microglia/alzheimers_coexpression_null_pvalues.csv index c4a5939..b5f2832 100644 --- a/manuscript/robustness/microglia/alzheimers_coexpression_null_pvalues.csv +++ b/manuscript/robustness/microglia/alzheimers_coexpression_null_pvalues.csv @@ -1,3 +1,3 @@ statistic,observed,null_mean,null_min,null_max,n_shuffles,p_value -mean_later_auc,0.9766878181883464,0.5034550251953028,0.4893086763151358,0.5307318817112471,25,0.038461538461538464 -first_auc,0.9239739374010474,0.5056706856655705,0.47839483619534773,0.5498843015467056,25,0.038461538461538464 +mean_later_auc,0.9766878181883464,0.5058179394833529,0.4880537421713261,0.5316342919379887,100,0.009900990099009901 +first_auc,0.9239739374010474,0.5033464864206552,0.459219339909877,0.5498843015467056,100,0.009900990099009901 diff --git a/manuscript/robustness/microglia/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/microglia/alzheimers_coexpression_null_runs.csv index 9b76213..b5b2dac 100644 --- a/manuscript/robustness/microglia/alzheimers_coexpression_null_runs.csv +++ b/manuscript/robustness/microglia/alzheimers_coexpression_null_runs.csv @@ -1,27 +1,102 @@ run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard -observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 -shuffle_1,MTCH1,0.5026001705029838,0.5029966164125346,20,max_iterations,0.015851137146795313,0.02377670572019297,0.0 -shuffle_2,TRPC7,0.5003592741444404,0.4957072268971993,20,max_iterations,0.016195727084769126,0.049276361130255,0.0 -shuffle_3,SCAPER,0.4938618925831202,0.49851153000633136,20,max_iterations,0.01757408683666437,0.09441764300482426,0.02564102564102564 -shuffle_4,B4GAT1-DT,0.4865424430641822,0.5094894788305333,20,max_iterations,0.022398345968297727,0.028945554789800137,0.02564102564102564 -shuffle_5,NAGK,0.5413347947874803,0.504336418719593,20,max_iterations,0.015506547208821502,0.051688490696071676,0.0 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b/manuscript/robustness/microglia/null_batches/shuffles_26-50/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,521 @@ +run,rank,gene +observed,1,SAT1 +observed,2,ST6GALNAC3 +observed,3,LINC00486 +observed,4,NEAT1 +observed,5,MEF2C +observed,6,ST6GAL1 +observed,7,LINC02798 +observed,8,LUC7L3 +observed,9,C3 +observed,10,NAV3 +observed,11,RBM25 +observed,12,ATRX +observed,13,PNISR +observed,14,LNCAROD +observed,15,SDK1 +observed,16,HSP90AA1 +observed,17,ENSG00000285534 +observed,18,SYNDIG1 +observed,19,PRPF38B +observed,20,CD74 +shuffle_26,1,MROH7-TTC4 +shuffle_26,2,GPR158 +shuffle_26,3,FMN2 +shuffle_26,4,TMSB4X +shuffle_26,5,ENSG00000276128 +shuffle_26,6,PRP4K +shuffle_26,7,ZDHHC11B +shuffle_26,8,CDH17 +shuffle_26,9,BLOC1S6 +shuffle_26,10,ENSG00000270792 +shuffle_26,11,GIPC3 +shuffle_26,12,SERP2 +shuffle_26,13,KLF11 +shuffle_26,14,LINC01934 +shuffle_26,15,HTR2A +shuffle_26,16,HERC2P3 +shuffle_26,17,ESR2 +shuffle_26,18,TPRN +shuffle_26,19,ENSG00000283692 +shuffle_26,20,SELENOP 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+shuffle_60,19,0.20537560303239144 diff --git a/manuscript/robustness/microglia/null_batches/shuffles_56-60/alzheimers_coexpression_null_panels.csv b/manuscript/robustness/microglia/null_batches/shuffles_56-60/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..f740697 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_56-60/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,121 @@ +run,rank,gene +observed,1,SAT1 +observed,2,ST6GALNAC3 +observed,3,LINC00486 +observed,4,NEAT1 +observed,5,MEF2C +observed,6,ST6GAL1 +observed,7,LINC02798 +observed,8,LUC7L3 +observed,9,C3 +observed,10,NAV3 +observed,11,RBM25 +observed,12,ATRX +observed,13,PNISR +observed,14,LNCAROD +observed,15,SDK1 +observed,16,HSP90AA1 +observed,17,ENSG00000285534 +observed,18,SYNDIG1 +observed,19,PRPF38B +observed,20,CD74 +shuffle_56,1,CTNND2 +shuffle_56,2,INTS11 +shuffle_56,3,ENSG00000254698 +shuffle_56,4,ENO2 +shuffle_56,5,ASIP +shuffle_56,6,SLFN12L +shuffle_56,7,BTBD8 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+shuffle_60,17,KCNK6 +shuffle_60,18,TTC6 +shuffle_60,19,SAT1 +shuffle_60,20,ITSN1 diff --git a/manuscript/robustness/microglia/null_batches/shuffles_56-60/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/microglia/null_batches/shuffles_56-60/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..af13f7d --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_56-60/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 +shuffle_56,CTNND2,0.5153513579344782,0.5156299120181097,20,max_iterations,0.021364576154376293,0.10682288077188146,0.0 +shuffle_57,CIP2A,0.5046888320545609,0.5236036628612075,20,max_iterations,0.01791867677463818,0.23259820813232254,0.0 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b/manuscript/robustness/microglia/null_batches/shuffles_61-65/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,121 @@ +run,rank,gene +observed,1,SAT1 +observed,2,ST6GALNAC3 +observed,3,LINC00486 +observed,4,NEAT1 +observed,5,MEF2C +observed,6,ST6GAL1 +observed,7,LINC02798 +observed,8,LUC7L3 +observed,9,C3 +observed,10,NAV3 +observed,11,RBM25 +observed,12,ATRX +observed,13,PNISR +observed,14,LNCAROD +observed,15,SDK1 +observed,16,HSP90AA1 +observed,17,ENSG00000285534 +observed,18,SYNDIG1 +observed,19,PRPF38B +observed,20,CD74 +shuffle_61,1,TREM2 +shuffle_61,2,CASC9 +shuffle_61,3,ENSG00000286883 +shuffle_61,4,TJP3 +shuffle_61,5,THSD4 +shuffle_61,6,APRT +shuffle_61,7,LINC01250 +shuffle_61,8,GALNTL6 +shuffle_61,9,AMZ2P1 +shuffle_61,10,ENSG00000287122 +shuffle_61,11,ANKRD33B +shuffle_61,12,ENSG00000249259 +shuffle_61,13,AKAP6 +shuffle_61,14,HEPACAM +shuffle_61,15,TEX41 +shuffle_61,16,ENSG00000286780 +shuffle_61,17,SNORD3A +shuffle_61,18,SCN4B +shuffle_61,19,KCNT2 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b/manuscript/robustness/microglia/null_batches/shuffles_61-65/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..23076b0 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_61-65/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 +shuffle_61,TREM2,0.5073864328340032,0.5105242429848751,20,max_iterations,0.022053756030323914,0.2749827705031013,0.0 +shuffle_62,GREM2,0.49895871392035074,0.5056420333649465,20,max_iterations,0.019641626464507237,0.23018607856650586,0.02564102564102564 +shuffle_63,A2MP1,0.5068749238825965,0.49098977561708507,20,max_iterations,0.015161957270847692,0.09476223294279806,0.0 +shuffle_64,ENSG00000249001,0.49951284861770795,0.5131923109822738,20,max_iterations,0.016540317022742935,0.22915230875258444,0.0 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+run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 +shuffle_66,LINC02798,0.4997746924856899,0.5161216426236969,20,max_iterations,0.01826326671261199,0.22777394900068917,0.02564102564102564 +shuffle_67,TSC22D1-AS1,0.5389477530142491,0.5121464785900399,20,max_iterations,0.013439007580978636,0.21054445210199862,0.0 +shuffle_68,CDH8,0.48456339057362074,0.49644493661381084,20,max_iterations,0.013439007580978636,0.013439007580978636,0.0 +shuffle_69,KLF11,0.5089635854341736,0.5260888513639552,20,max_iterations,0.014817367332873881,0.030323914541695383,0.0 +shuffle_70,TMPRSS5,0.4963646328096456,0.5137572071504793,20,max_iterations,0.019297036526533425,0.11199172984148863,0.0 diff --git a/manuscript/robustness/microglia/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/microglia/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..6e5bdd8 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_71-75/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,110 @@ +run,iteration,flip_rate +observed,2,0.3542384562370779 +observed,3,0.04066161268090972 +observed,4,0.014128187456926258 +observed,5,0.025499655410062026 +observed,6,0.01757408683666437 +observed,7,0.02722260509993108 +observed,8,0.02033080634045486 +observed,9,0.013783597518952447 +observed,10,0.015161957270847692 +observed,11,0.015161957270847692 +observed,12,0.014472777394900068 +observed,13,0.01171605789110958 +observed,14,0.013783597518952447 +observed,15,0.01757408683666437 +observed,16,0.011371467953135768 +observed,17,0.01791867677463818 +observed,18,0.011026878015161957 +observed,19,0.013783597518952447 +observed,20,0.006891798759476223 +shuffle_71,2,0.04135079255685734 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100644 index 0000000..c4bbaa7 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_76-80/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,121 @@ +run,rank,gene +observed,1,SAT1 +observed,2,ST6GALNAC3 +observed,3,LINC00486 +observed,4,NEAT1 +observed,5,MEF2C +observed,6,ST6GAL1 +observed,7,LINC02798 +observed,8,LUC7L3 +observed,9,C3 +observed,10,NAV3 +observed,11,RBM25 +observed,12,ATRX +observed,13,PNISR +observed,14,LNCAROD +observed,15,SDK1 +observed,16,HSP90AA1 +observed,17,ENSG00000285534 +observed,18,SYNDIG1 +observed,19,PRPF38B +observed,20,CD74 +shuffle_76,1,ENSG00000232451 +shuffle_76,2,CHAT +shuffle_76,3,MAP7 +shuffle_76,4,TMEM132D +shuffle_76,5,DDIT4-AS1 +shuffle_76,6,BHLHE41 +shuffle_76,7,MTCH1 +shuffle_76,8,HIVEP3 +shuffle_76,9,TOX2 +shuffle_76,10,ENSG00000287122 +shuffle_76,11,NELL1 +shuffle_76,12,USP43 +shuffle_76,13,CTNNA3 +shuffle_76,14,LINC01229 +shuffle_76,15,ENSG00000227579 +shuffle_76,16,MOG +shuffle_76,17,C4orf51 +shuffle_76,18,GRID2 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b/manuscript/robustness/microglia/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..0a183fb --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_76-80/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 +shuffle_76,ENSG00000232451,0.5097978321763489,0.5170530423127471,20,max_iterations,0.019986216402481046,0.03824948311509304,0.0 +shuffle_77,GULP1,0.5130982827913775,0.49999802289322154,20,max_iterations,0.015851137146795313,0.1946933149552033,0.0 +shuffle_78,SNHG25,0.480690537084399,0.5062613642901106,20,max_iterations,0.022742935906271536,0.22605099931082012,0.02564102564102564 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b/manuscript/robustness/microglia/null_batches/shuffles_86-90/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..c04be87 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_86-90/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,110 @@ +run,iteration,flip_rate +observed,2,0.3542384562370779 +observed,3,0.04066161268090972 +observed,4,0.014128187456926258 +observed,5,0.025499655410062026 +observed,6,0.01757408683666437 +observed,7,0.02722260509993108 +observed,8,0.02033080634045486 +observed,9,0.013783597518952447 +observed,10,0.015161957270847692 +observed,11,0.015161957270847692 +observed,12,0.014472777394900068 +observed,13,0.01171605789110958 +observed,14,0.013783597518952447 +observed,15,0.01757408683666437 +observed,16,0.011371467953135768 +observed,17,0.01791867677463818 +observed,18,0.011026878015161957 +observed,19,0.013783597518952447 +observed,20,0.006891798759476223 +shuffle_86,2,0.04514128187456926 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+shuffle_90,7,AFF3 +shuffle_90,8,TMC5 +shuffle_90,9,REXO2 +shuffle_90,10,RNASET2 +shuffle_90,11,SNTG1 +shuffle_90,12,TMEM37 +shuffle_90,13,TRMT10A +shuffle_90,14,MAPK4 +shuffle_90,15,GFAP +shuffle_90,16,INTS4P1 +shuffle_90,17,ENSG00000250771 +shuffle_90,18,CARD18 +shuffle_90,19,DDX46 +shuffle_90,20,SHISA6 diff --git a/manuscript/robustness/microglia/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/microglia/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..7c86649 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_86-90/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 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-0,0 +1,110 @@ +run,iteration,flip_rate +observed,2,0.3542384562370779 +observed,3,0.04066161268090972 +observed,4,0.014128187456926258 +observed,5,0.025499655410062026 +observed,6,0.01757408683666437 +observed,7,0.02722260509993108 +observed,8,0.02033080634045486 +observed,9,0.013783597518952447 +observed,10,0.015161957270847692 +observed,11,0.015161957270847692 +observed,12,0.014472777394900068 +observed,13,0.01171605789110958 +observed,14,0.013783597518952447 +observed,15,0.01757408683666437 +observed,16,0.011371467953135768 +observed,17,0.01791867677463818 +observed,18,0.011026878015161957 +observed,19,0.013783597518952447 +observed,20,0.006891798759476223 +shuffle_91,2,0.04410751206064783 +shuffle_91,3,0.04203997243280496 +shuffle_91,4,0.04583046175051689 +shuffle_91,5,0.03618194348725017 +shuffle_91,6,0.03893866299104066 +shuffle_91,7,0.03652653342522398 +shuffle_91,8,0.0368711233631978 +shuffle_91,9,0.03893866299104066 +shuffle_91,10,0.02343211578221916 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b/manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_panels.csv new file mode 100644 index 0000000..68117a4 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_panels.csv @@ -0,0 +1,121 @@ +run,rank,gene +observed,1,SAT1 +observed,2,ST6GALNAC3 +observed,3,LINC00486 +observed,4,NEAT1 +observed,5,MEF2C +observed,6,ST6GAL1 +observed,7,LINC02798 +observed,8,LUC7L3 +observed,9,C3 +observed,10,NAV3 +observed,11,RBM25 +observed,12,ATRX +observed,13,PNISR +observed,14,LNCAROD +observed,15,SDK1 +observed,16,HSP90AA1 +observed,17,ENSG00000285534 +observed,18,SYNDIG1 +observed,19,PRPF38B +observed,20,CD74 +shuffle_91,1,PAMR1 +shuffle_91,2,PRP4K +shuffle_91,3,SNHG32 +shuffle_91,4,PRMT8 +shuffle_91,5,PDE4D +shuffle_91,6,SLC4A10 +shuffle_91,7,OPALIN +shuffle_91,8,VSNL1 +shuffle_91,9,CD1D +shuffle_91,10,FBXL2 +shuffle_91,11,ZDHHC11B +shuffle_91,12,NECAB1 +shuffle_91,13,FSTL5 +shuffle_91,14,ENSG00000222031 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a/manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..de79bb0 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_91-95/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 +shuffle_91,PAMR1,0.5085616855437828,0.5077605012879932,20,max_iterations,0.02343211578221916,0.05961405926946933,0.0 +shuffle_92,MIR137HG,0.5233467299963463,0.5084056329221888,20,max_iterations,0.019641626464507237,0.20537560303239144,0.0 +shuffle_93,SORCS2,0.48355255145536474,0.5039342223598896,20,max_iterations,0.01757408683666437,0.28049620951068227,0.0 +shuffle_94,ZNF483,0.49607843137254903,0.49747797568856156,20,max_iterations,0.021709166092350102,0.18297725706409373,0.0 +shuffle_95,ASIP,0.5224150529777127,0.5128917260208276,20,max_iterations,0.019641626464507237,0.0585802894555479,0.0 diff --git a/manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_flip_rates.csv b/manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_flip_rates.csv new file mode 100644 index 0000000..774a79d --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_flip_rates.csv @@ -0,0 +1,110 @@ +run,iteration,flip_rate +observed,2,0.3542384562370779 +observed,3,0.04066161268090972 +observed,4,0.014128187456926258 +observed,5,0.025499655410062026 +observed,6,0.01757408683666437 +observed,7,0.02722260509993108 +observed,8,0.02033080634045486 +observed,9,0.013783597518952447 +observed,10,0.015161957270847692 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+observed,3,LINC00486 +observed,4,NEAT1 +observed,5,MEF2C +observed,6,ST6GAL1 +observed,7,LINC02798 +observed,8,LUC7L3 +observed,9,C3 +observed,10,NAV3 +observed,11,RBM25 +observed,12,ATRX +observed,13,PNISR +observed,14,LNCAROD +observed,15,SDK1 +observed,16,HSP90AA1 +observed,17,ENSG00000285534 +observed,18,SYNDIG1 +observed,19,PRPF38B +observed,20,CD74 +shuffle_96,1,NDN +shuffle_96,2,ZDHHC4 +shuffle_96,3,DYM-AS1 +shuffle_96,4,TMEM237 +shuffle_96,5,CFAP77 +shuffle_96,6,LINC03122 +shuffle_96,7,BEND6 +shuffle_96,8,AFF3 +shuffle_96,9,COLEC11 +shuffle_96,10,PRAMEF6 +shuffle_96,11,DNAH3 +shuffle_96,12,ENSG00000259925 +shuffle_96,13,H2BC5 +shuffle_96,14,ZBTB7A +shuffle_96,15,ENSG00000249776 +shuffle_96,16,C5orf67 +shuffle_96,17,NRXN3 +shuffle_96,18,COPS6 +shuffle_96,19,STARD8 +shuffle_96,20,ENSG00000255557 +shuffle_97,1,ENSG00000258654 +shuffle_97,2,LINC02147 +shuffle_97,3,LINC01320 +shuffle_97,4,ENSG00000270792 +shuffle_97,5,CXCR4 +shuffle_97,6,GABRD +shuffle_97,7,PSD2 +shuffle_97,8,PATL2 +shuffle_97,9,CCDC171 +shuffle_97,10,ENSG00000250149 +shuffle_97,11,ENSG00000287292 +shuffle_97,12,IGSF22 +shuffle_97,13,ENSG00000262213 +shuffle_97,14,ELAVL3 +shuffle_97,15,KCNV1 +shuffle_97,16,FIGN +shuffle_97,17,SYT10 +shuffle_97,18,ENSG00000285987 +shuffle_97,19,LRRC7 +shuffle_97,20,TPD52L1 +shuffle_98,1,MYO9A +shuffle_98,2,GRID1 +shuffle_98,3,PDLIM2 +shuffle_98,4,AKAP9 +shuffle_98,5,ACSM3 +shuffle_98,6,FBXL2 +shuffle_98,7,TESPA1 +shuffle_98,8,AMZ2P1 +shuffle_98,9,UPB1 +shuffle_98,10,ENSG00000285835 +shuffle_98,11,LINC01117 +shuffle_98,12,CLNK +shuffle_98,13,FAM133A +shuffle_98,14,HPGDS +shuffle_98,15,SLITRK5 +shuffle_98,16,GALNT13 +shuffle_98,17,GNGT1 +shuffle_98,18,SRRM2 +shuffle_98,19,CRYZL1 +shuffle_98,20,ENSG00000276128 +shuffle_99,1,NAGK +shuffle_99,2,TRNAU1AP +shuffle_99,3,SLC12A2 +shuffle_99,4,ENSG00000259200 +shuffle_99,5,ENSG00000255241 +shuffle_99,6,ENSG00000267748 +shuffle_99,7,PPM1E +shuffle_99,8,RXFP1 +shuffle_99,9,DTX1 +shuffle_99,10,SUSD4 +shuffle_99,11,CLSTN2 +shuffle_99,12,BEND6 +shuffle_99,13,PEBP1 +shuffle_99,14,ZNF568 +shuffle_99,15,GIPC3 +shuffle_99,16,LINC00871 +shuffle_99,17,PRICKLE1 +shuffle_99,18,DNM1 +shuffle_99,19,FYB2 +shuffle_99,20,PLCB1 +shuffle_100,1,LINC00504 +shuffle_100,2,NEXMIF +shuffle_100,3,B3GALT1 +shuffle_100,4,ZNF804B +shuffle_100,5,ENSG00000287231 +shuffle_100,6,FAAH2 +shuffle_100,7,LARP7 +shuffle_100,8,OLFM3 +shuffle_100,9,SRP14 +shuffle_100,10,SYN2 +shuffle_100,11,GPX1 +shuffle_100,12,PRKG1 +shuffle_100,13,C14orf178 +shuffle_100,14,SPOCK1 +shuffle_100,15,LINC01182 +shuffle_100,16,LINC01500 +shuffle_100,17,BBOF1 +shuffle_100,18,ENSG00000237480 +shuffle_100,19,LARP1B +shuffle_100,20,AP2A2 diff --git a/manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_runs.csv b/manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_runs.csv new file mode 100644 index 0000000..d23d709 --- /dev/null +++ b/manuscript/robustness/microglia/null_batches/shuffles_96-100/alzheimers_coexpression_null_runs.csv @@ -0,0 +1,7 @@ +run,first_gene,first_auc,mean_later_auc,n_genes,stop_reason,min_flip_rate,final_flip_rate,jaccard +observed,SAT1,0.9239739374010474,0.9766878181883464,20,threshold,0.006891798759476223,0.006891798759476223,1.0 +shuffle_96,NDN,0.48115942028985514,0.5108991095376499,20,max_iterations,0.016195727084769126,0.1140592694693315,0.0 +shuffle_97,ENSG00000258654,0.517811472415053,0.5097348887055808,20,max_iterations,0.02446588559614059,0.10854583046175052,0.0 +shuffle_98,MYO9A,0.5077822433321154,0.4931144338563796,20,max_iterations,0.015506547208821502,0.03480358373535493,0.0 +shuffle_99,NAGK,0.508226768968457,0.5128917706598084,20,max_iterations,0.01757408683666437,0.22639558924879394,0.0 +shuffle_100,LINC00504,0.47197052734137135,0.504690399665594,20,max_iterations,0.019641626464507237,0.026878015161957272,0.0 diff --git a/manuscript/runtime_benchmark.py b/manuscript/runtime_benchmark.py index e0d8265..b71e805 100644 --- a/manuscript/runtime_benchmark.py +++ b/manuscript/runtime_benchmark.py @@ -1,15 +1,20 @@ """Runtime of recursieve on simulated data of increasing size. Runs recursieve 3 times on simulated datasets of 2000 genes and 5k to 25k -cells. Existing results in the output folder are resumed. +cells. Every run does exactly N_ITERATIONS iterations (stopping rules off): +with the defaults, the number of iterations depends on the seed and varies +more than tenfold, which hides how runtime scales with dataset size. Existing +results in the output folder are resumed; runs made with a different +N_ITERATIONS are ignored. Usage: python runtime_benchmark.py [--out-dir DIR] Outputs (to DIR): - runtime_benchmark_results.csv: Runtime of every run -- runtime_benchmark_summary.csv: Mean, SD, min, and max per size -- runtime_benchmark_barplot.png / .pdf +- runtime_benchmark_summary.csv: Mean, SD, median, min, and max per size +- runtime_benchmark_barplot.png / .pdf: median runtime, min-max range, and + each run as a dot """ from __future__ import annotations @@ -30,6 +35,7 @@ GENE_COUNT = 2000 CELL_COUNTS = [5000, 10000, 15000, 20000, 25000] N_REPEATS = 3 +N_ITERATIONS = 20 # iterations per run, fixed so runs are comparable def build_dataset(ncells: int, ngenes: int, seed: int) -> ad.AnnData: @@ -54,10 +60,13 @@ def build_dataset(ncells: int, ngenes: int, seed: int) -> ad.AnnData: return adata -def time_recursieve(adata: ad.AnnData, seed: int) -> float: - """Run recursieve once and return runtime in seconds.""" +def time_recursieve(adata: ad.AnnData, seed: int) -> tuple[float, int]: + """Run recursieve once for N_ITERATIONS iterations. + + Returns runtime in seconds and the number of genes selected. + """ start = time.perf_counter() - _ = recursieve.recursieve( + model = recursieve.recursieve( adata.copy(), group1="group_1", group2="group_2", @@ -66,37 +75,47 @@ def time_recursieve(adata: ad.AnnData, seed: int) -> float: print_to_console=False, n_jobs=-1, seed=seed, + # stopping rules off: a negative threshold disables the threshold and + # oscillation stops, and patience=None disables the patience stop + max_iterations=N_ITERATIONS, + flip_rate_percentage=-1.0, + patience=None, ) - return time.perf_counter() - start + return time.perf_counter() - start, len(model.genes) -def plot_results(summary_df: pd.DataFrame, out_png: Path, out_pdf: Path) -> None: - """Bar plot of mean runtime with SD error bars.""" +def plot_results(summary_df: pd.DataFrame, results_df: pd.DataFrame, + out_png: Path, out_pdf: Path) -> None: + """Bar plot of median runtime with min-max range and every run as a dot.""" sns.set_theme(style="whitegrid", context="talk") fig, ax = plt.subplots(figsize=(10, 6)) - plot_df = summary_df.sort_values("cells").copy() - plot_df["cells_label"] = plot_df["cells"].map(lambda x: f"{x // 1000}k") - - palette = ["#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a"] - ax.bar( - plot_df["cells_label"], - plot_df["mean_runtime_sec"], - yerr=plot_df["std_runtime_sec"], - capsize=6, - color=palette, - edgecolor="black", - linewidth=1.0, - ) - - ax.set_title("recursieve Runtime vs Dataset Size", pad=12, weight="bold") + plot_df = summary_df.sort_values("cells").reset_index(drop=True) + labels = [f"{x // 1000}k" for x in plot_df["cells"]] + x = range(len(plot_df)) + median = plot_df["median_runtime_sec"] + # min-max range around the median (never below zero, unlike mean +/- SD) + yerr = [median - plot_df["min_runtime_sec"], plot_df["max_runtime_sec"] - median] + ax.bar(x, median, yerr=yerr, capsize=6, color="#94b6d8", edgecolor="black", + linewidth=1.0, error_kw={"elinewidth": 1.2}) + for i, n_cells in enumerate(plot_df["cells"]): + runs = results_df.loc[results_df["cells"] == n_cells, "runtime_sec"] + ax.scatter([i] * len(runs), runs, s=25, color="#1a1d21", zorder=3) + + ax.set_xticks(list(x)) + ax.set_xticklabels(labels) + ax.set_title(f"recursieve Runtime vs Dataset Size ({N_ITERATIONS} iterations)", + pad=12, weight="bold") ax.set_xlabel("Number of Cells (2000 genes)") ax.set_ylabel("Runtime (seconds)") + ax.set_ylim(bottom=0) ax.spines["top"].set_visible(False) ax.spines["right"].set_visible(False) - for i, val in enumerate(plot_df["mean_runtime_sec"]): - ax.text(i, val, f"{val:.1f}s", ha="center", va="bottom", fontsize=10) + # median label just above the top of the range + pad = 0.02 * plot_df["max_runtime_sec"].max() + for i, (val, top) in enumerate(zip(median, plot_df["max_runtime_sec"])): + ax.text(i, top + pad, f"{val:.1f}s", ha="center", va="bottom", fontsize=10) fig.tight_layout() fig.savefig(out_png, dpi=300) @@ -120,9 +139,15 @@ def main() -> None: if results_path.exists(): existing = pd.read_csv(results_path) + # only runs made with this iteration count can be combined + if "iterations" in existing.columns: + existing = existing[existing["iterations"] == N_ITERATIONS] + else: + existing = existing.iloc[0:0] rows = existing.to_dict("records") done = {(int(r["cells"]), int(r["replicate"])) for r in rows} - print(f"Resuming from existing results: {len(rows)} completed runs") + print(f"Resuming from existing results: {len(rows)} completed runs " + f"with {N_ITERATIONS} iterations") for i, n_cells in enumerate(CELL_COUNTS): dataset_seed = 1000 + i @@ -135,8 +160,8 @@ def main() -> None: continue run_seed = 2000 + i * 10 + rep print(f" Running recursieve replicate {rep}/{N_REPEATS} (seed={run_seed})") - runtime_sec = time_recursieve(adata=adata, seed=run_seed) - print(f" Runtime: {runtime_sec:.3f} sec") + runtime_sec, n_selected = time_recursieve(adata=adata, seed=run_seed) + print(f" Runtime: {runtime_sec:.3f} sec ({n_selected} genes selected)") rows.append( { "cells": n_cells, @@ -144,6 +169,8 @@ def main() -> None: "replicate": rep, "dataset_seed": dataset_seed, "run_seed": run_seed, + "iterations": N_ITERATIONS, + "genes_selected": n_selected, "runtime_sec": runtime_sec, } ) @@ -154,12 +181,13 @@ def main() -> None: summary_df = ( results_df.groupby(["cells", "genes"], as_index=False)["runtime_sec"] - .agg(["mean", "std", "min", "max"]) + .agg(["mean", "std", "median", "min", "max"]) .reset_index() .rename( columns={ "mean": "mean_runtime_sec", "std": "std_runtime_sec", + "median": "median_runtime_sec", "min": "min_runtime_sec", "max": "max_runtime_sec", } @@ -167,10 +195,10 @@ def main() -> None: ) summary_df.to_csv(summary_path, index=False) - plot_results(summary_df, out_png=plot_png, out_pdf=plot_pdf) + plot_results(summary_df, results_df, out_png=plot_png, out_pdf=plot_pdf) print(summary_df.round(2).to_string(index=False)) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/src/recursieve/recursieve.py b/src/recursieve/recursieve.py index f46449e..6a26c4d 100644 --- a/src/recursieve/recursieve.py +++ b/src/recursieve/recursieve.py @@ -1063,7 +1063,8 @@ def jaccard(a, b): } return self.seed_stability_results - def coexpression_null(self, n_shuffles=25, max_iterations=None, n_parallel=None, verbose=0): + def coexpression_null(self, n_shuffles=25, max_iterations=None, n_parallel=None, verbose=0, + first_shuffle=1): """ Rerun selection on data with gene-gene co-expression destroyed. @@ -1093,7 +1094,7 @@ def coexpression_null(self, n_shuffles=25, max_iterations=None, n_parallel=None, Parameters ---------- n_shuffles : int, optional - Number of independently shuffled matrices. Default is 3. + Number of independently shuffled matrices. Default is 25. max_iterations : int, optional Override the number of iterations of the shuffled runs. Default is None (len(self.genes) - 1, so shuffled panels match the @@ -1105,6 +1106,11 @@ def coexpression_null(self, n_shuffles=25, max_iterations=None, n_parallel=None, verbose : int, optional joblib progress level; 10 or more reports every finished shuffle. Default is 0 (silent). + first_shuffle : int, optional + Index of the first shuffle. Shuffle k uses seed (seed, k) and is + named shuffle_k, so a later call with first_shuffle = previous + n_shuffles + 1 adds new shuffles without repeating old ones. + Default is 1. Returns ------- @@ -1160,12 +1166,12 @@ def coexpression_null(self, n_shuffles=25, max_iterations=None, n_parallel=None, results = Parallel(n_jobs=n_parallel, verbose=verbose)( delayed(recursieve._run_null_shuffle)( X, obs, self.adata.var_names, params, (self.seed, k), self.genes) - for k in range(1, n_shuffles + 1) + for k in range(first_shuffle, first_shuffle + n_shuffles) ) rows = [self._null_summary(self, "observed", self.genes)] curves = [("observed", i + 2, f) for i, f in enumerate(self.flip_rates)] panels = [("observed", i + 1, g) for i, g in enumerate(self.genes)] - for k, (summary, flips, genes) in enumerate(results, start=1): + for k, (summary, flips, genes) in enumerate(results, start=first_shuffle): name = f"shuffle_{k}" rows.append({"run": name, **summary}) curves += [(name, i + 2, f) for i, f in enumerate(flips)] From d30b4b7d89b9883c0afdfa29e3a224f7e86f0d5d Mon Sep 17 00:00:00 2001 From: kennypavan Date: Mon, 12 Oct 2026 17:42:00 -0700 Subject: [PATCH 2/2] removed sh file --- manuscript/robustness/run_full.sh | 50 ------------------------------- 1 file changed, 50 deletions(-) delete mode 100755 manuscript/robustness/run_full.sh diff --git a/manuscript/robustness/run_full.sh b/manuscript/robustness/run_full.sh deleted file mode 100755 index 7776c8f..0000000 --- a/manuscript/robustness/run_full.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env bash -# Run every analysis step for each group. Progress goes to logs/status.txt and -# step output to logs/_.log. A failed step skips the rest of its group. -# With no arguments all four groups run. To run one group, pass its folder name -# and cell selection options, e.g. run_full.sh microglia --cell-type Microglia. -# Run detached: -# setsid nohup manuscript/robustness/run_full.sh > /dev/null 2>&1 < /dev/null & -cd "$(dirname "$0")/../.." -ROOT=manuscript/robustness -mkdir -p $ROOT/logs -export PYTHONPATH=src -PY="env/bin/python -u manuscript" -status() { date "+$1 %F %T" >> $ROOT/logs/status.txt; } - -run_group() { - local name=$1 dir=$ROOT/$1; shift - local sel="$*" - mkdir -p $dir - rm -f $dir/alzheimers_* - status "$name start" - local steps=( - "analysis|$PY/alzheimers_analysis.py --out-dir $dir --n-seeds 10 $sel" - "null|$PY/alzheimers_coexpression_null.py --out-dir $dir --n-shuffles 25 $sel" - "de_plots|$PY/alzheimers_de_plots.py --out-dir $dir" - "rf_baseline|$PY/alzheimers_rf_baseline.py --out-dir $dir --max-depth 10 $sel" - "rf_venn|$PY/alzheimers_rf_vs_recursieve_venn.py --out-dir $dir" - "seed_plot|$PY/alzheimers_robustness_plots.py --out-dir $dir" - "summary|$PY/alzheimers_summary.py --out-dir $dir" - ) - for step in "${steps[@]}"; do - local label=${step%%|*} cmd=${step#*|} - status "$name $label start" - if $cmd > $ROOT/logs/${name}_${label}.log 2>&1; then - status "$name $label done" - else - status "$name $label FAILED" - return 1 - fi - done - status "$name done" -} - -if [ $# -gt 0 ]; then - run_group "$@" - exit -fi -run_group excitatory --cell-type Excitatory --n-cells 25000 --balance-groups -run_group inhibitory --cell-type Inhibitory --n-cells 25000 --balance-groups -run_group astrocytes --cell-type Astrocytes --n-cells 7818 --balance-groups -run_group all_cell_types --n-cells 50000