assignCelltype {ccfindR} | R Documentation |
Computes GSEA enrichment score of marker sets in meta gene list
assignCelltype(obj, rank, gset, gene_names = NULL, p = 0, remove.na = FALSE, p.value = FALSE, nperm = 1000, progress.bar = TRUE, grp.prefix = c("IG"))
obj |
Object of class |
rank |
Rank to examine |
gset |
List of gene sets to be used as markers |
gene_names |
Names of genes to be used for meta-gene identification |
p |
Enrichment score exponent. |
remove.na |
Remove gene sets with no overlap |
p.value |
Estimatte p values using permutation |
nperm |
No. of permutation replicates |
progress.bar |
Display progress bar for p value computation |
grp.prefix |
Gene name prefix to search for with wildcard matches in query |
If obj
is of clas scNMFSet
, it computes meta gene list using
meta_gene.cv
. Otherwise, obj
is expected to be
a data frame of the same structure as the output of meta_gene.cv
;
the number of rows same as the total number of metagenes per cluster,
three columns per each cluster (gene name, meta-gene score, and coefficient of variation).
The argument gset
is a list of gene sets to be checked for enrichment in
each cluster meta gene list. The enrichment score is computed using
the GSEA algorithm (Subramanian et al. 2005).
Matrix of enrichment score statistics with cell types in rows and clusters in columns
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005). “Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.” Proc. Natl. Acad. Sci., USA, 102, 15545–15550. doi: 10.1073/pnas.0506580102.
dir <- system.file('extdata',package='ccfindR') pbmc <- read_10x(dir) pbmc <- vb_factorize(pbmc, ranks=5) meta <- meta_gene.cv(pbmc,rank=5, gene_names=rowData(pbmc)[,2]) markers <- list('B cell'=c('CD74','IG','HLA'), 'CD8+ T'=c('CD8A','CD8B','GZMK','CCR7','LTB'), 'CD4+ T'=c('CD3D','CD3E','IL7R','LEF1'), 'NK'=c('GNLY','NKG7','GZMA','GZMH'), 'Macrophage'=c('S100A8','S100A9','CD14','LYZ','CFD')) gsea <- assignCelltype(meta, rank=5, gset=markers, grp.prefix=c('IG','HLA')) gsea