Get cell type specificity of gene expression

For each gene, compute fraction of overall expression attributable to each cell type

Usage

cellTypeSpecificity(pb, ...)

Arguments

pb

SingleCellExperiment of pseudobulk data where easy assay is a cell type.

...

other arguments passed to edgeR::calcNormFactors()

Value

matrix of the fraction of expression attributable to each cell type for each gene.

Details

Sum counts for each cell type, and compute the fraction of counts-per-million attributable to each cell type for each gene

Examples

library(muscat)
library(SingleCellExperiment)

data(example_sce)

# create pseudobulk for each sample and cell cluster
pb <- aggregateToPseudoBulk(example_sce,
  assay = "counts",
  cluster_id = "cluster_id",
  sample_id = "sample_id",
  verbose = FALSE
)

# Compute cell type specificity of each gene
df <- cellTypeSpecificity(pb)

# Violin plot of specificity scores for each cell type
# Dashed line indicates genes that are equally expressed
# across all cell types.  For K cell types, this is 1/K
plotViolin(df)


# Compute the maximum specificity score for each gene
scoreMax <- apply(df, 1, max)
head(scoreMax)
#>       HES4      ISG15   AURKAIP1     MRPL20      SSU72       RER1 
#>   585.0935 61555.5769  1012.0795   644.9976   835.4867  1049.8381 

# For each cell type, get most specific gene
genes <- rownames(df)[apply(df, 2, which.max)]

# Barplot of 5 genes
plotPercentBars(df, genes = genes)


# heatmap of 5 genes that are most cell type specific
dreamlet::plotHeatmap(df, genes = genes)