diff --git a/Metagenomics/Illumina/Pipeline_GL-DPPD-7107_Versions/GL-DPPD-7107-B.md b/Metagenomics/Illumina/Pipeline_GL-DPPD-7107_Versions/GL-DPPD-7107-B.md index 87cd4c77..f5ce8ab9 100644 --- a/Metagenomics/Illumina/Pipeline_GL-DPPD-7107_Versions/GL-DPPD-7107-B.md +++ b/Metagenomics/Illumina/Pipeline_GL-DPPD-7107_Versions/GL-DPPD-7107-B.md @@ -202,7 +202,7 @@ Software Updates and Changes: | samtools | 1.23.1 | [https://github.com/samtools/samtools#samtools](https://github.com/samtools/samtools#samtools) | | SPAdes | 4.2.0 | [https://github.com/ablab/spades](https://github.com/ablab/spades) | | R | 4.5.3 | [https://www.r-project.org](https://www.r-project.org) | -| dplyr | 1.2.0 | [https://dplyr.tidyverse.org](https://dplyr.tidyverse.org) | +| dplyr | 1.2.1 | [https://dplyr.tidyverse.org](https://dplyr.tidyverse.org) | | ggplot2 | 4.0.2 | [https://ggplot2.tidyverse.org](https://ggplot2.tidyverse.org) | | glue | 1.8.0 | [https://glue.tidyverse.org](https://glue.tidyverse.org) | | htmlwidgets | 1.6.4 | [http://www.htmlwidgets.org](http://www.htmlwidgets.org) | @@ -1112,7 +1112,7 @@ library(tidyr) cols <- colnames(df) index <- grep(end_col, cols) start <- grep(end_col, cols) + 1 - end <- (length(cols) - index) + end <- length(cols) df_samples <- cols[start:end] sample_names <- intersect(df_samples, sample_names) @@ -3236,7 +3236,7 @@ humann_renorm_table -o Gene-families-KO-cpm_GLmetagenomics.tsv --update-snames #### 20g. Combine MetaPhlan Taxonomy Tables ```bash -merge_metaphlan_tables.py *-humann3-out-dir/*_humann_temp/*_metaphlan_bugs_list.tsv > Metaphlan-taxonomy_GLmetagenomics.tsv +merge_metaphlan_tables.py *-humann3-out-dir/*_humann_temp/*_metaphlan_bugs_list.tsv > metaphlan-taxonomy_GLmetagenomics.tsv ``` **Parameter Definitions:** @@ -3253,7 +3253,7 @@ merge_metaphlan_tables.py *-humann3-out-dir/*_humann_temp/*_metaphlan_bugs_list. **Output data:** -- **Metaphlan-taxonomy_GLmetagenomics.tsv** (MetaPhlan estimated taxonomic relative abundances) +- **metaphlan-taxonomy_GLmetagenomics.tsv** (MetaPhlan estimated taxonomic relative abundances) #### 20h. Create MetaPhlan Species Count Table @@ -3496,7 +3496,7 @@ write_tsv(x = table2write, file = "Pathway-abundances_filtered_GLmetagenomics.ts - Pathway-abundances_unfiltered_GLmetagenomics.tsv (pathway abundances normalized to copies-per-million, with cleaned headers) - **Gene-families-KO_filtered_GLmetagenomics.tsv** (KO term abundances filtered for features with less than 500 CPM across samples) - **Gene-families-uniref_filtered_GLmetagenomics.tsv** (gene-family abundances filtered for features with less than 500 CPM across samples) -- **Gene-families-KO_filtered_GLmetagenomics.tsv** (Pathway abundances filtered for features with less than 500 CPM across samples) +- **Pathway-abundances_filtered_GLmetagenomics.tsv** (Pathway abundances filtered for features with less than 500 CPM across samples) #### 20l. Create Humann Function Heatmaps diff --git a/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7116_Versions/GL-DPPD-7116.md b/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7116_Versions/GL-DPPD-7116.md index 82bf5848..02078693 100644 --- a/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7116_Versions/GL-DPPD-7116.md +++ b/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7116_Versions/GL-DPPD-7116.md @@ -171,13 +171,13 @@ Barbara Novak (GeneLab Data Processing Lead) | MetaPhlAn | 4.1.0 | [https://github.com/biobakery/MetaPhlAn](https://github.com/biobakery/MetaPhlAn) | | Minimap2 | 2.30 | [https://github.com/lh3/minimap2](https://github.com/lh3/minimap2) | | MultiQC | 1.27.1 | [https://multiqc.info/](https://multiqc.info/) | -| NanoPlot | 1.45.2 | [https://github.com/wdecoster/NanoPlot](https://github.com/wdecoster/NanoPlot) | +| NanoPlot | 1.46.2 | [https://github.com/wdecoster/NanoPlot](https://github.com/wdecoster/NanoPlot) | | Porechop | 0.2.4 | [https://github.com/rrwick/Porechop](https://github.com/rrwick/Porechop) | | Prodigal | 2.6.3 | [https://github.com/hyattpd/Prodigal#prodigal](https://github.com/hyattpd/Prodigal#prodigal) | | samtools | 1.23.1 | [https://github.com/samtools/samtools#samtools](https://github.com/samtools/samtools#samtools) | | R | 4.5.3 | [https://www.r-project.org](https://www.r-project.org) | | decontam | 1.30.0 | [https://www.bioconductor.org/packages/release/bioc/html/decontam.html](https://www.bioconductor.org/packages/release/bioc/html/decontam.html) | -| dplyr | 1.2.0 | [https://dplyr.tidyverse.org](https://dplyr.tidyverse.org) | +| dplyr | 1.2.1 | [https://dplyr.tidyverse.org](https://dplyr.tidyverse.org) | | ggplot2 | 4.0.2 | [https://ggplot2.tidyverse.org](https://ggplot2.tidyverse.org) | | glue | 1.8.0 | [https://glue.tidyverse.org](https://glue.tidyverse.org) | | htmlwidgets | 1.6.4 | [http://www.htmlwidgets.org](http://www.htmlwidgets.org) | @@ -1630,7 +1630,6 @@ library(tidyr) ) ) sub_metadata[, freq_col] <- as.numeric(sub_metadata[, freq_col]) - sub_metadata[, prev_col] <- tolower(sub_metadata[, prev_col]) } @@ -1696,14 +1695,44 @@ library(tidyr) feature_table <- feature_table[, -1] %>% as.matrix() # Prepare metadata - metadata <- read_delim(metadata_file) %>% as.data.frame + metadata <- read_delim(metadata_file) + if (!is.null(prevalence_column) && prevalence_column %in% colnames(metadata)) { + metadata <- metadata %>% + mutate(!!prevalence_column := as.character(!!sym(prevalence_column)) %>% + tolower()) %>% + as.data.frame() + } else { + metadata <- metadata %>% as.data.frame() + } row.names(metadata) <- metadata[, samples_column] + samples <- intersect(colnames(feature_table), rownames(metadata)) + metadata <- metadata[samples, ] + feature_table <- feature_table[, samples] + + if (classification_method == "gene-function") { + + name <- "Combined-gene-level-KO-function" + + } else if (classification_method == "gene-taxonomy") { + + name <- "Combined-gene-level-taxonomy" + + } else if (classification_method == "contig-taxonomy") { + + name <- "Combined-contig-level-taxonomy" + + } else { + + name <- classification_method + + } + # Run decontam - # Assign prev and freq column names to NULL if the values in the supplied columns aren't unique - if( length(unique(metadata[, prev_col])) == 1) prev_col <- NULL - if( length(unique(metadata[, freq_col])) == 1) freq_col <- NULL - contamdf <- run_decontam(feature_table, metadata, threshold, prev_col, freq_col, ntc_name) + # Assign prevalence and frequency_column column names to NULL if the values in the supplied columns aren't unique + if( length(unique(metadata[, prevalence_column])) == 1) prevalence_column <- NULL + if( length(unique(metadata[, frequency_column])) == 1) frequency_column <- NULL + contamdf <- run_decontam(feature_table, metadata, threshold, prevalence_column, frequency_column, ntc_name) contamdf <- as.data.frame(contamdf) %>% rownames_to_column(feature_column) @@ -1711,9 +1740,11 @@ library(tidyr) if (classification_method == 'gene-function') { type <- "KO" } # Write decontaminated feature table and decontam's primary results - outfile <- glue("{output_prefix}_decontam_results{assay_suffix}.tsv") + outfile <- glue("{output_prefix}{name}_decontam_results{assay_suffix}.tsv") write_tsv(x = contamdf, file = outfile) + taxonomy_methods <- c("kaiju", "kraken2", "metaphlan", "gene-taxonomy", "contig-taxonomy") + # Get the list of contaminants identified by decontam contaminants <- contamdf %>% filter(contaminant == TRUE) %>% @@ -1732,9 +1763,13 @@ library(tidyr) negate = TRUE)) rownames(decontaminated_table) <- decontaminated_table[[feature_column]] - decontaminated_table <- decontaminated_table[,-1] %>% as.matrix - outfile <- glue("{output_prefix}_decontam_{type}_table{assay_suffix}.tsv") + # Add _species string to output file name if it is a taxonomy method + if (any(grep(pattern = classification_method, x = taxonomy_methods))) { + outfile <- glue("{output_prefix}{name}_decontam_species_table{assay_suffix}.tsv") + } else { + outfile <- glue("{output_prefix}{name}_decontam_table{assay_suffix}.tsv") + } write_tsv(x = decontaminated_table, file = outfile) return(decontaminated_table) @@ -1765,7 +1800,10 @@ library(tidyr) - `assay_suffix` - a character string specifying the GeneLab assay suffix (default: "_GLlblMetag") **Output Data:** - - {output_prefix}_decontam_{species|KO}_table_GLlblMetag.tsv - decontaminated feature table file + - {output_prefix}_decontam_{species_table|table}_GLlblMetag.tsv - decontaminated feature table file, for + taxonomy classification methods the filename + includes the "species" keyword, other classification + methods omit the "species" keyword - {output_prefix}_decontam_results_GLlblMetag.tsv - Decontam results file **Returns:** dataframe, `decontaminated_table`, containing the decontaminated feature table @@ -1891,9 +1929,8 @@ library(tidyr) get_samples <- function(assembly_table_df, sample_names, end_col='species') { # Get common samples cols <- colnames(df) - index <- grep(end_col, cols) start <- grep(end_col, cols) + 1 - end <- (length(cols) - index) + end <- length(cols) df_samples <- cols[start:end] sample_names <- intersect(df_samples, sample_names) @@ -3036,7 +3073,7 @@ feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlblMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Gene-families-uniref_decontam_species_table_GLlblMetag.tsv", + feature_table_file = "Gene-families-uniref_decontam_table_GLlblMetag.tsv", samples_column="sample_id", group_column = "group", output_prefix = "Gene-families-uniref_decontam", assay_suffix = "_GLlblMetag", @@ -3056,7 +3093,7 @@ feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlblMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Gene-families-KO_decontam_species_table_GLlblMetag.tsv", + feature_table_file = "Gene-families-KO_decontam_table_GLlblMetag.tsv", samples_column="sample_id", group_column = "group", output_prefix = "Gene-families-KO_decontam", assay_suffix = "_GLlblMetag", @@ -3076,7 +3113,7 @@ feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlblMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Pathway-abundances_decontam_species_table_GLlblMetag.tsv", + feature_table_file = "Pathway-abundances_decontam_table_GLlblMetag.tsv", samples_column="sample_id", group_column = "group", output_prefix = "Pathway-abundances_decontam", assay_suffix = "_GLlblMetag", @@ -3102,15 +3139,15 @@ make_heatmap(metadata_table_file = metadata_table, **Output Data:** -- **Gene-family-uniref_decontam_results_GLlblMetag.tsv** (decontam's result table for gene-family abundances, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-uniref_decontam_species_table_GLlblMetag.tsv** (decontaminated gene-family abundances table, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-uniref_decontam_species_heatmap_GLlblMetag.png** (heatmap of decontaminated gene-family abundances, output from [make_heatmap()](#make_heatmap)) -- **Gene-family-KO_decontam_results_GLlblMetag.tsv** (decontam's result table KO term abundances, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-KO_decontam_species_table_GLlblMetag.tsv** (decontaminated KO term abundances table, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-KO_decontam_species_heatmap_GLlblMetag.png** (heatmap of decontaminated KO term abundances, output from [make_heatmap()](#make_heatmap)) +- **Gene-families-uniref_decontam_results_GLlblMetag.tsv** (decontam's result table for gene-family abundances, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-uniref_decontam_table_GLlblMetag.tsv** (decontaminated gene-family abundances table, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-uniref_decontam_heatmap_GLlblMetag.png** (heatmap of decontaminated gene-family abundances, output from [make_heatmap()](#make_heatmap)) +- **Gene-families-KO_decontam_results_GLlblMetag.tsv** (decontam's result table KO term abundances, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-KO_decontam_table_GLlblMetag.tsv** (decontaminated KO term abundances table, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-KO_decontam_heatmap_GLlblMetag.png** (heatmap of decontaminated KO term abundances, output from [make_heatmap()](#make_heatmap)) - **Pathway-abundances_decontam_results_GLlblMetag.tsv** (decontam's result table, output from [feature_decontam()](#feature_decontam)) -- **Pathway-abundances_decontam_species_table_GLlblMetag.tsv** (decontaminated species table, output from [feature_decontam()](#feature_decontam)) -- **Pathway-abundances_decontam_species_heatmap_GLlblMetag.png** (barplot after filtering out contaminants, output from [make_heatmap()](#make_heatmap)) +- **Pathway-abundances_decontam_table_GLlblMetag.tsv** (decontaminated species table, output from [feature_decontam()](#feature_decontam)) +- **Pathway-abundances_decontam_heatmap_GLlblMetag.png** (barplot after filtering out contaminants, output from [make_heatmap()](#make_heatmap))
@@ -4359,7 +4396,7 @@ make_heatmap(metadata_table_file = metadata_table, > Note: species_table and heatmaps are only generated if 1 or more contaminants were detected ```R -feature_table_file <- "genes-KO-functions_table.tsv" +feature_table_file <- "Combined-gene-level-KO-function_unfiltered_GLlblMetag.tsv" metadata_table <- "/path/to/sample/metadata" # Prepare metadata @@ -4380,7 +4417,7 @@ decontaminated_table <- feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlblMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Combined-gene-level-KO-function_decontam_KO_table_GLlblMetag.tsv", + feature_table_file = "Combined-gene-level-KO-function_decontam_table_GLlblMetag.tsv", samples_column = "sample_id", group_column = "group", output_prefix = "Combined-gene-level-KO-function_decontam", assay_suffix = "_GLlblMetag", @@ -4405,7 +4442,7 @@ make_heatmap(metadata_table_file = metadata_table, **Output Data:** - **Combined-gene-level-KO-function_decontam_results_GLlblMetag.tsv** (decontam results table, output from [feature_decontam()](#feature_decontam)) -- **Combined-gene-level-KO-function_decontam_KO_table_GLlblMetag.tsv** (decontaminated gene-level KO functions table, output from [feature_decontam()](#feature_decontam)) +- **Combined-gene-level-KO-function_decontam_table_GLlblMetag.tsv** (decontaminated gene-level KO functions table, output from [feature_decontam()](#feature_decontam)) - **Combined-gene-level-KO-function_decontam_heatmap_GLlblMetag.png** (heatmap of all gene-level KO function assignments after filtering out contaminants, output from [make_heatmap()](#make_heatmap)) - **Combined-gene-level-KO-function_decontam_top_50_heatmap_GLlblMetag.png** (heatmap of the top 50 gene-level KO function assignments after filtering out contaminants, output from [make_heatmap()](#make_heatmap)) @@ -4463,7 +4500,7 @@ make_heatmap(metadata_table_file = metadata_table, **Input data:** - assembly-summaries_GLlblMetag.tsv (table of assembly summary statistics, output from [Step 15b](#15b-summarize-assemblies)) - Combined-contig-level-taxonomy-coverages-CPM_GLlblMetag.tsv (table with all samples combined based on contig-level - taxonomic classifications, output from [Step 22](#22-combine-contig-level-coverage-and-taxonomy-for-each-sample)) + taxonomic classifications, output from [Step 23b](#23b-generate-contig-level-coverage-summary-tables)) **Output data:** - Combined-contig-level-taxonomy_unfiltered_GLlblMetag.tsv (aggregated contig-level taxonomy table with samples in columns and species in rows) @@ -4473,7 +4510,7 @@ make_heatmap(metadata_table_file = metadata_table, #### 26h. Contig-level Feature Filtering ```R -feature_table_file <- "Combined-contig-level-taxonomy_GLlblMetag.tsv" +feature_table_file <- "Combined-contig-level-taxonomy_unfiltered_GLlblMetag.tsv" metadata_table <- "/path/to/sample/metadata" threshold <- 1000 @@ -4563,7 +4600,7 @@ make_heatmap(metadata_table_file = metadata_table, **Input Data:** -- `Combined-contig-level-taxonomy_GLlblMetag.tsv`(aggregated contig taxonomy table with samples in columns and species in rows, from [Step 26g](#26g-contig-level-heatmaps)) +- `Combined-contig-level-taxonomy_unfiltered_GLlblMetag.tsv`(aggregated contig taxonomy table with samples in columns and species in rows, from [Step 26g](#26g-contig-level-heatmaps)) - `/path/to/sample/metadata` (a file containing sample-wise metadata, mapping sample names to group metadata) **Output Data:** diff --git a/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7117_Versions/GL-DPPD-7117.md b/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7117_Versions/GL-DPPD-7117.md index f126b6c6..15140daa 100644 --- a/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7117_Versions/GL-DPPD-7117.md +++ b/Metagenomics/Low_Biomass/Pipeline_GL-DPPD-7117_Versions/GL-DPPD-7117.md @@ -167,7 +167,7 @@ Barbara Novak (GeneLab Data Processing Lead) | SPAdes | 4.2.0 | [https://github.com/ablab/spades](https://github.com/ablab/spades) | | R | 4.5.3 | [https://www.r-project.org](https://www.r-project.org) | | decontam | 1.30.0 | [https://www.bioconductor.org/packages/release/bioc/html/decontam.html](https://www.bioconductor.org/packages/release/bioc/html/decontam.html) | -| dplyr | 1.2.0 | [https://dplyr.tidyverse.org](https://dplyr.tidyverse.org) | +| dplyr | 1.2.1 | [https://dplyr.tidyverse.org](https://dplyr.tidyverse.org) | | ggplot2 | 4.0.2 | [https://ggplot2.tidyverse.org](https://ggplot2.tidyverse.org) | | glue | 1.8.0 | [https://glue.tidyverse.org](https://glue.tidyverse.org) | | htmlwidgets | 1.6.4 | [http://www.htmlwidgets.org](http://www.htmlwidgets.org) | @@ -1257,7 +1257,6 @@ library(tidyr) ) ) sub_metadata[, freq_col] <- as.numeric(sub_metadata[, freq_col]) - sub_metadata[, prev_col] <- tolower(sub_metadata[, prev_col]) } @@ -1318,19 +1317,56 @@ library(tidyr) threshold = 0.5, classification_method, output_prefix, assay_suffix = "_GLlbsMetag") { # Prepare feature table - feature_table <- read_delim(feature_table_file) %>% as.data.frame + feature_table <- read_delim(feature_table_file) %>% as.data.frame rownames(feature_table) <- feature_table[[1]] feature_table <- feature_table[, -1] %>% as.matrix() # Prepare metadata metadata <- read_delim(metadata_file) %>% as.data.frame + # Prepare feature table + feature_table <- read_delim(feature_table_file) %>% as.data.frame + rownames(feature_table) <- feature_table[[1]] + feature_table <- feature_table[, -1] %>% as.matrix() + + # Prepare metadata + metadata <- read_delim(metadata_file) + if (!is.null(prevalence_column) && prevalence_column %in% colnames(metadata)) { + metadata <- metadata %>% + mutate(!!prevalence_column := as.character(!!sym(prevalence_column)) %>% + tolower()) %>% + as.data.frame() + } else { + metadata <- metadata %>% as.data.frame() + } row.names(metadata) <- metadata[, samples_column] + samples <- intersect(colnames(feature_table), rownames(metadata)) + metadata <- metadata[samples, ] + feature_table <- feature_table[, samples] + + if (classification_method == "gene-function") { + + name <- "Combined-gene-level-KO-function" + + } else if (classification_method == "gene-taxonomy") { + + name <- "Combined-gene-level-taxonomy" + + } else if (classification_method == "contig-taxonomy") { + + name <- "Combined-contig-level-taxonomy" + + } else { + + name <- classification_method + + } + # Run decontam - # Assign prev and freq column names to NULL if the values in the supplied columns aren't unique - if(length(unique(metadata[, prev_col])) == 1) prev_col <- NULL - if(length(unique(metadata[, freq_col])) == 1) freq_col <- NULL - contamdf <- run_decontam(feature_table, metadata, threshold, prev_col, freq_col, ntc_name) + # Assign prevalence and frequency_column column names to NULL if the values in the supplied columns aren't unique + if( length(unique(metadata[, prevalence_column])) == 1) prevalence_column <- NULL + if( length(unique(metadata[, frequency_column])) == 1) frequency_column <- NULL + contamdf <- run_decontam(feature_table, metadata, threshold, prevalence_column, frequency_column, ntc_name) contamdf <- as.data.frame(contamdf) %>% rownames_to_column(feature_column) @@ -1338,9 +1374,11 @@ library(tidyr) if (classification_method == 'gene-function') { type <- "KO" } # Write decontaminated feature table and decontam's primary results - outfile <- glue("{output_prefix}_decontam_results{assay_suffix}.tsv") + outfile <- glue("{output_prefix}{name}_decontam_results{assay_suffix}.tsv") write_tsv(x = contamdf, file = outfile) + taxonomy_methods <- c("kaiju", "kraken2", "metaphlan", "gene-taxonomy", "contig-taxonomy") + # Get the list of contaminants identified by decontam contaminants <- contamdf %>% filter(contaminant == TRUE) %>% @@ -1359,9 +1397,13 @@ library(tidyr) negate = TRUE)) rownames(decontaminated_table) <- decontaminated_table[[feature_column]] - decontaminated_table <- decontaminated_table[,-1] %>% as.matrix - outfile <- glue("{output_prefix}_decontam_{type}_table{assay_suffix}.tsv") + # Add _species string to output file name if it is a taxonomy method + if (any(grep(pattern = classification_method, x = taxonomy_methods))) { + outfile <- glue("{output_prefix}{name}_decontam_species_table{assay_suffix}.tsv") + } else { + outfile <- glue("{output_prefix}{name}_decontam_table{assay_suffix}.tsv") + } write_tsv(x = decontaminated_table, file = outfile) return(decontaminated_table) @@ -1392,7 +1434,10 @@ library(tidyr) - `assay_suffix` - a character string specifying the GeneLab assay suffix (default: "_GLlbsMetag") **Output Data:** - - {output_prefix}_decontam_{species|KO}_table_GLlbsMetag.tsv - decontaminated feature table file + - {output_prefix}_decontam_{species_table|table}_GLlblMetag.tsv - decontaminated feature table file, for + taxonomy classification methods the filename + includes the "species" keyword, other classification + methods omit the "species" keyword - {output_prefix}_decontam_results_GLlbsMetag.tsv - Decontam results file **Returns:** dataframe, `decontaminated_table`, containing the decontaminated feature table @@ -1514,9 +1559,8 @@ library(tidyr) get_samples <- function(assembly_table_df, sample_names, end_col='species') { # Get common samples cols <- colnames(df) - index <- grep(end_col, cols) start <- grep(end_col, cols) + 1 - end <- (length(cols) - index) + end <- length(cols) df_samples <- cols[start:end] sample_names <- intersect(df_samples, sample_names) @@ -2491,10 +2535,10 @@ humann_renorm_table -o Gene-families-KO-cpm_GLlbsMetag.tsv --update-snames #### 8g. Combine MetaPhlan Taxonomy Tables ```bash -merge_metaphlan_tables.py *-humann3-out-dir/*_humann_temp/*_metaphlan_bugs_list.tsv > Metaphlan-taxonomy_GLlbsMetag.tsv +merge_metaphlan_tables.py *-humann3-out-dir/*_humann_temp/*_metaphlan_bugs_list.tsv > metaphlan-taxonomy_GLlbsMetag.tsv # remove redundant text from headers -sed -i 's/_metaphlan_bugs_list//g' Metaphlan-taxonomy_GLlbsMetag.tsv +sed -i 's/_metaphlan_bugs_list//g' metaphlan-taxonomy_GLlbsMetag.tsv ``` **Parameter Definitions:** @@ -2511,7 +2555,7 @@ sed -i 's/_metaphlan_bugs_list//g' Metaphlan-taxonomy_GLlbsMetag.tsv **Output Data:** -- **Metaphlan-taxonomy_GLlbsMetag.tsv** (MetaPhlan estimated taxonomic relative abundances) +- **metaphlan-taxonomy_GLlbsMetag.tsv** (MetaPhlan estimated taxonomic relative abundances) #### 8h. Create MetaPhlan Species Count Table @@ -2806,7 +2850,7 @@ write_tsv(x = table2write, file = "Pathway-abundances_filtered_GLlbsMetag.tsv") - Pathway-abundances_unfiltered_GLlbsMetag.tsv (pathway abundances normalized to copies-per-million, with cleaned headers) - **Gene-families-KO_filtered_GLlbsMetag.tsv** (KO term abundances filtered for features with less than 500 CPM across samples) - **Gene-families-uniref_filtered_GLlbsMetag.tsv** (gene-family abundances filtered for features with less than 500 CPM across samples) -- **Gene-families-KO_filtered_GLlbsMetag.tsv** (Pathway abundances filtered for features with less than 500 CPM across samples) +- **Pathway-abundances_filtered_GLlbsMetag.tsv** (Pathway abundances filtered for features with less than 500 CPM across samples) #### 8m. Create Humann Function Heatmaps @@ -2912,7 +2956,7 @@ feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlbsMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Gene-families-uniref_decontam_species_table_GLlbsMetag.tsv", + feature_table_file = "Gene-families-uniref_decontam_table_GLlbsMetag.tsv", samples_column="sample_id", group_column = "group", output_prefix = "Gene-families-uniref_decontam", assay_suffix = "_GLlbsMetag", @@ -2932,7 +2976,7 @@ feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlbsMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Gene-families-KO_decontam_species_table_GLlbsMetag.tsv", + feature_table_file = "Gene-families-KO_decontam_table_GLlbsMetag.tsv", samples_column="sample_id", group_column = "group", output_prefix = "Gene-families-KO_decontam", assay_suffix = "_GLlbsMetag", @@ -2952,7 +2996,7 @@ feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlbsMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Pathway-abundances_decontam_species_table_GLlbsMetag.tsv", + feature_table_file = "Pathway-abundances_decontam_table_GLlbsMetag.tsv", samples_column="sample_id", group_column = "group", output_prefix = "Pathway-abundances_decontam", assay_suffix = "_GLlbsMetag", @@ -2978,15 +3022,15 @@ make_heatmap(metadata_table_file = metadata_table, **Output Data:** -- **Gene-family-uniref_decontam_results_GLlbsMetag.tsv** (decontam's result table for gene-family abundances, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-uniref_decontam_species_table_GLlbsMetag.tsv** (decontaminated gene-family abundances table, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-uniref_decontam_species_heatmap_GLlbsMetag.png** (heatmap of decontaminated gene-family abundances, output from [make_heatmap()](#make_heatmap)) -- **Gene-family-KO_decontam_results_GLlbsMetag.tsv** (decontam's result table KO term abundances, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-KO_decontam_species_table_GLlbsMetag.tsv** (decontaminated KO term abundances table, output from [feature_decontam()](#feature_decontam)) -- **Gene-family-KO_decontam_species_heatmap_GLlbsMetag.png** (heatmap of decontaminated KO term abundances, output from [make_heatmap()](#make_heatmap)) +- **Gene-families-uniref_decontam_results_GLlbsMetag.tsv** (decontam's result table for gene-family abundances, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-uniref_decontam_table_GLlbsMetag.tsv** (decontaminated gene-family abundances table, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-uniref_decontam_heatmap_GLlbsMetag.png** (heatmap of decontaminated gene-family abundances, output from [make_heatmap()](#make_heatmap)) +- **Gene-families-KO_decontam_results_GLlbsMetag.tsv** (decontam's result table KO term abundances, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-KO_decontam_table_GLlbsMetag.tsv** (decontaminated KO term abundances table, output from [feature_decontam()](#feature_decontam)) +- **Gene-families-KO_decontam_heatmap_GLlbsMetag.png** (heatmap of decontaminated KO term abundances, output from [make_heatmap()](#make_heatmap)) - **Pathway-abundances_decontam_results_GLlbsMetag.tsv** (decontam's result table, output from [feature_decontam()](#feature_decontam)) -- **Pathway-abundances_decontam_species_table_GLlbsMetag.tsv** (decontaminated species table, output from [feature_decontam()](#feature_decontam)) -- **Pathway-abundances_decontam_species_heatmap_GLlbsMetag.png** (barplot after filtering out contaminants, output from [make_heatmap()](#make_heatmap)) +- **Pathway-abundances_decontam_table_GLlbsMetag.tsv** (decontaminated species table, output from [feature_decontam()](#feature_decontam)) +- **Pathway-abundances_decontam_heatmap_GLlbsMetag.png** (barplot after filtering out contaminants, output from [make_heatmap()](#make_heatmap))
@@ -4051,7 +4095,7 @@ make_heatmap(metadata_table_file = metadata_table, > Note: species_table and heatmaps are only generated if 1 or more contaminants were detected ```R -feature_table_file <- "Combined-gene-level-taxonomy_GLlbsMetag.tsv" +feature_table_file <- "Combined-gene-level-taxonomy_unfiltered_GLlbsMetag.tsv" metadata_table <- "/path/to/sample/metadata" decontaminated_table <- feature_decontam(metadata_file = metadata_table, @@ -4087,7 +4131,7 @@ make_heatmap(metadata_table_file = metadata_table, **Input Data:** -- `Combined-gene-level-taxonomy_GLlbsMetag.tsv`(aggregated gene taxonomy table with samples in columns and species in rows, from [Step 21a](#21a-gene-level-taxonomy-heatmaps)) +- `Combined-gene-level-taxonomy_unfiltered_GLlbsMetag.tsv`(aggregated gene taxonomy table with samples in columns and species in rows, from [Step 21a](#21a-gene-level-taxonomy-heatmaps)) - `/path/to/sample/metadata` (a file containing sample-wise metadata, mapping sample names to group metadata) **Output Data:** @@ -4230,7 +4274,7 @@ decontaminated_table <- feature_decontam(metadata_file = metadata_table, assay_suffix = "_GLlbsMetag") make_heatmap(metadata_table_file = metadata_table, - feature_table_file = "Combined-gene-level-KO-function_decontam_KO_table_GLlbsMetag.tsv", + feature_table_file = "Combined-gene-level-KO-function_decontam_table_GLlbsMetag.tsv", samples_column = "sample_id", group_column = "group", output_prefix = "Combined-gene-level-KO-function_decontam", assay_suffix = "_GLlbsMetag", @@ -4256,7 +4300,7 @@ make_heatmap(metadata_table_file = metadata_table, **Output Data:** - **Combined-gene-level-KO-function_decontam_results_GLlbsMetag.tsv** (decontam results table, output from [feature_decontam()](#feature_decontam)) -- **Combined-gene-level-KO-function_decontam_KO_table_GLlbsMetag.tsv** (decontaminated gene-level KO functions table, output from [feature_decontam()](#feature_decontam)) +- **Combined-gene-level-KO-function_decontam_table_GLlbsMetag.tsv** (decontaminated gene-level KO functions table, output from [feature_decontam()](#feature_decontam)) - **Combined-gene-level-KO-function_decontam_heatmap_GLlbsMetag.png** (heatmap of all gene-level KO function assignments after filtering out contaminants, output from [make_heatmap()](#make_heatmap)) - **Combined-gene-level-KO-function_decontam_top_50_heatmap_GLlbsMetag.png** (heatmap of the top 50 gene-level KO function assignments after filtering out contaminants, output from [make_heatmap()](#make_heatmap))