scGSVA provides wrap functions to do GSVA analysis for single-cell data. And scGSVA includes functions to build annotation for almost all species. scGSVA also provides functions to generate figures based on the GSVA results.
scGSVA provides functions to generate annotation data which can be used in the analysis.
Fixed some issues since there was a bug due to the KEGG annotation changed these days.
Add batch calculation when the cell population is huge.
Add UCell function within the scgsva function. Users need to install UCell by using BiocManager::install("UCell").
Add spatialFeaturePlot function for spatial transcriptomics.
library(devtools)
install_github("guokai8/scGSVA")
set.seed(123)
library(scGSVA)
data(pbmcs)
hsko<-buildAnnot(species="human",keytype="SYMBOL",anntype="KEGG")
res<-scgsva(pbmcs,hsko,method="ssgsea") ## or use UCell
vlnPlot(res,features="Wnt.signaling.pathway",group_by="groups") ## split.plot = TRUE and split.by
dotPlot(res,features="Wnt.signaling.pathway",group_by="groups")
ridgePlot(res,features="Wnt.signaling.pathway",group_by="groups")
featurePlot(res,features="Wnt.signaling.pathway", reduction="tsne", group_by="groups")
Heatmap(res,group_by="groups")
## Find significant pathways across groups
findPathway(res,group = "groups")
sigPathway(res, group = "groups")
## Extract specific pathways with expression value
genes(res, features = "Wnt.signaling.pathway")
The scGSVA package uses the GSVA package to do the GSVA analysis for the single cell data. The package is still under development.
For any questions please contact guokai8@gmail.com or https://github.com/guokai8/scGSVA/issues
Try to do multiple p-value corrections within group-wise other than overall.