Deconvolve Immune Microenvironment Using xCell

Estimates immune cell fractions using the xCell algorithm. xCell provides cell type enrichment scores for 64 immune and stromal cell types from gene expression data.

Usage

deconvo_xcell(eset, project = NULL, arrays = FALSE)

Arguments

eset: Gene expression matrix with HGNC gene symbols as row names and samples as columns.
project: Optional project name to add as `ProjectID` column. Default is `NULL`.
arrays: Logical indicating microarray data (`TRUE`) or RNA-seq (`FALSE`). Default is `FALSE`.

Value

Data frame with xCell enrichment scores. Cell type columns are suffixed with `_xCell`.

Author

Dongqiang Zeng

Examples

eset_stad <- load_data("eset_stad")
anno_grch38 <- load_data("anno_grch38")
eset <- anno_eset(eset = eset_stad, annotation = anno_grch38, probe = "id")
#> ℹ Row number of original eset: 60483
#> ✔ 100% of probes in expression set were annotated
#> ℹ Found 2293 duplicate symbols, using "mean" method
#> ℹ Row number after filtering duplicated gene symbol: 50181
xcell_result <- deconvo_xcell(eset = eset[, 1:3], project = "TCGA-STAD")
#> ℹ Running xCell deconvolution
#> ℹ Number of genes: 10783
#> ℹ GSVA version 2.4.8
#> ℹ Searching for rows with constant values
#> ℹ Calculating GSVA ranks
#> ℹ kcdf='auto' (default)
#> ℹ GSVA dense (classical) algorithm
#> ℹ Row-wise ECDF estimation with Gaussian kernels
#> ℹ Calculating row ECDFs
#> ℹ Calculating column ranks
#> ℹ GSVA dense (classical) algorithm
#> ℹ Calculating GSVA scores
#> ✔ Calculations finished
head(xcell_result)[, 1:5]
#>             ID ProjectID Adipocytes_xCell Astrocytes_xCell B-cells_xCell
#> 1 TCGA-BR-6455 TCGA-STAD     3.755491e-23     0.000000e+00  0.000000e+00
#> 2 TCGA-BR-7196 TCGA-STAD     1.794635e-23     3.702040e-22  1.311341e-23
#> 3 TCGA-BR-8371 TCGA-STAD     0.000000e+00     1.881182e-21  3.149663e-08