PASBench

Introduction

Single-cell RNA sequencing (scRNA-seq) analysis enables researchers to uncover more refined and novel cell clusters, which have greatly advanced our understanding of cellular states. There were many state-of-art computational tools developed for clustering cells, identifying marker genes, and visualizing scRNA-seq data. However, biological interpretation of the clustering results remains a big challenge. Pathway activity scores (PASs) analysis has been applied to transform the gene-level data into explainable gene sets representing biological processes or pathways to uncover the potential mechanism of cell heterogeneity. To the best of our knowledge, there were no systematic benchmark studies to evaluate the performance of these unsupervised PAS transformation algorithms.

This reposity contain seven PAS tools and evaluation metrics

Evaluation scheme

Detials

pathway

human

Name Detials Number of gene sets
hallmarker Hallmark gene sets 50
CGP genetic and chemical perturbations 3297
biocarta BioCarta pathway database 289
kegg KEGG pathway database 186
PID PID pathway database 196
reactome Reactome pathway database 1532
TFT transcriptional factor targets 1137
CGN cancer gene neighborhoods 427
CM cancer models 431
GO.bp GO biological process 7530
GO.cc Co cellular Component 999
GO.mf GO molecular fucntion 1663
OncoG oncogenic signatures 189
Immu immunologic signatures 4872
panther protein annotation through evolutionary relationship 94
humancyc human metabonomics 127

mouse

Name Detials Number of gene sets
kegg KEGG pathway database 259
PID PID pathway database 193
panther protein annotation through evolutionary relationship 151
mousecyc mouse metabonomics 321
biocarta BioCarta pathway database 176
reactome Reactome pathway database 4342
TFT transcriptional factor targets 373
GO.bp GO biological process 8203
GO.cc Co cellular Component 1082
GO.mf GO molecular fucntion 3240