testSctpa

A portable toolkit for pathway activity score calculation

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. Pathway activity score (PAS) 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. This package provide six portable interfaces for PAS calculation tools and abundant pathway databases in human and mouse.

Install

devtools::install_github('zgyaru/testSctpa')

Dependencies

# install.packages("GSVA")
# devtools::install_github("YosefLab/VISION")
BiocManager::install("AUCell")

Quite start

Step 1. loading test data

library(testSctpa)
library(Seurat)

counts = load_counts()
### you could load your own data:
#counts = read.table("folder/expression_counts.txt.gz",
#                     header = TRUE,
#                     sep = '\t',
#                     row.names = 1)
se_oj = CreateSeuratObject(counts)

Step 2. Calculating pathway activity score

se_oj = cal_PAS(seurat_object = se_oj,
              tool = 'AUCell',   ## GSVA, ssGSEA, plage, zscore or Vision
              normalize = 'log',
              species = 'mouse', 
              pathway='kegg')

Step 3. clustering cells using PAS by Seurat

se_oj = FindVariableFeatures(se_oj, verbose = FALSE)
se_oj = ScaleData(se_oj)
se_oj = RunPCA(se_oj)
se_oj = RunUMAP(se_oj,dims = 1:8)
se_oj = FindNeighbors(se_oj)
se_oj = FindClusters(se_oj)
DimPlot(se_oj)

Step 4. cell-type specific pathways

library(dplyr)
markers = FindAllMarkers(se_oj,logfc.threshold = 0)
pathways = markers %>% group_by(cluster) %>% top_n(n=5,wt=avg_log2FC)  #For Seurat>4.0, set "wt=avg_logFC"
DoHeatmap(se_oj,features=pathways$gene)

Pathway Detials

human

Name	Detials	Number of gene sets
kegg	KEGG pathway database	186
scSignature	cell type signature	671
hallmarker	Hallmark gene sets	50
CGP	genetic and chemical perturbations	3297
biocarta	BioCarta pathway database	289
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
ASCN2	retinoblastoma protein	102
pharmgkb	pharmacogenomics	37

mouse

Name	Detials	Number of gene sets
kegg	KEGG pathway database	259
panther	protein annotation through evolutionary relationship	151
mousecyc	mouse metabonomics	321
reactome	Reactome pathway database	1396
TFT	transcriptional factor targets	373
GO.bp	GO biological process	8203
GO.cc	Co cellular Component	1082
GO.mf	GO molecular fucntion	3240
drug	drug related	844

Reference

[1] Hänzelmann S, Castelo R, Guinney J (2013). “GSVA: gene set variation analysis for microarray and RNA-Seq data.” BMC Bioinformatics.

[2] Aibar S, Bravo Gonzalez-Blas C, Moerman T, Huynh-Thu V, Imrichova H, Hulselmans G, Rambow F, Marine J, Geurts P, Aerts J, van den Oord J, Kalender Atak Z, Wouters J, Aerts S (2017). “SCENIC: Single-Cell Regulatory Network Inference And Clustering.” Nature Methods.

[3] DeTomaso D, Jones MG, Subramaniam M, Ashuach T, Ye CJ, Yosef N (2019). Functional interpretation of single cell similarity maps. Nat Communication.