Deconvolve Using CIBERSORT — deconvo

CIBERSORT is freely available to academic users. License and binary can be obtained from https://cibersortx.stanford.edu.

Usage

deconvo_cibersort(
  eset,
  project = NULL,
  arrays = FALSE,
  perm = 1000,
  absolute = FALSE,
  abs_method = "sig.score",
  parallel = FALSE,
  num_cores = 2,
  seed = NULL
)

Arguments

eset: Expression matrix with gene symbols as row names.
project: Optional project name. Default is `NULL`.
arrays: Logical: optimized for microarray data. Default is `FALSE`.
perm: Permutations for statistical analysis. Default is 1000.
absolute: Logical: run in absolute mode. Default is `FALSE`.
abs_method: Method for absolute mode: `"sig.score"` or `"no.sumto1"`. Default is `"sig.score"`.
parallel: Enable parallel execution. Default is `FALSE`.
num_cores: Number of cores for parallel mode. Default is 2.
seed: Random seed for reproducibility. Default is `NULL`.

Value

Data frame with CIBERSORT cell fractions. Columns suffixed with `_CIBERSORT`.

Author

Dongqiang Zeng

Examples

eset_tme_stad <- load_data("eset_tme_stad")
lm22 <- load_data("lm22")
cibersort_result <- deconvo_cibersort(
  eset = eset_tme_stad,
  project = "TCGA-STAD",
  perm = 100
)
#> ℹ Running CIBERSORT