assignCelltype {ccfindR}R Documentation

Cell type assignment via GSEA

Description

Computes GSEA enrichment score of marker sets in meta gene list

Usage

assignCelltype(obj, rank, gset, gene_names = NULL, p = 0,
  remove.na = FALSE, p.value = FALSE, nperm = 1000,
  progress.bar = TRUE, grp.prefix = c("IG"))

Arguments

obj

Object of class scNMFSet.

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

Details

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).

Value

Matrix of enrichment score statistics with cell types in rows and clusters in columns

References

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.

Examples

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

[Package ccfindR version 1.10.0 Index]