update description

Author: ghar1821

src/metrics/ratio_inconsistent_peaks/config.vsh.yaml

@@ -16,14 +16,21 @@ info:
       # Can contain only lowercase letters or underscores.
     - name: ratio_inconsistent_peaks
       label: Ratio of inconsistent peaks
-      summary: "Ratio of the number of cell‑type marker‑expression peaks between validation and batch‑normalized data."
+      summary: "Ratio of the number of cell‑type marker‑expression peaks between unintegrated and batch‑normalized data."
       description: |
-        The metric compares the number of cell type specific marker expression peaks between the validation and batch-normalized data. 
-        The number of peaks is calculated using the `scipy.signal.find_peaks` function. 
-        The metric is calculated as the absolute difference between the number of peaks in the validation and batch-normalized data.
+        The metric compares the number of cell type specific marker expression peaks between unintegrated and batch normalized data.
+        The number of peaks is calculated using the `scipy.signal.find_peaks` function.
+        The metric is calculated as the absolute difference between the number of peaks in the unintegrated and batch-normalized data.
         The (cell type) marker expression profiles are first smoothed using kernel density estimation (KDE) (`scipy.stats.gaussian_kde`),
         and then peaks are then identified using the `scipy.signal.find_peaks` function.
         For peak calling, the `prominence` parameter is set to 0.1 and the `height` parameter is set to 0.05*max_density.
+        Ratio of inconsistent peaks is defined as number of cases where the number of peaks differ between the two splits in the batch
+        normalized data divided by the total number of cases.
+        Cases where there are different number of peaks between the two splits in the unintegrated data are ignored from the denominator.
+        A lower score indicates better performance, means there are less cases with inconsistent peaks after batch correction.
+        An alternative peak counting method using persistent homology is also implemented for comparison because peak calling 
+        is sensitive to noise and parameter choices.
+        
       references:
         doi: 
           - 10.1038/s41592-019-0686-2