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