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Main TME Deconvolution Function

Source: R/deconvo_tme.R
deconvo_tme.Rd

Unified interface for multiple TME deconvolution methods.

Usage

deconvo_tme(
  eset,
  project = NULL,
  method = tme_deconvolution_methods,
  arrays = FALSE,
  tumor = TRUE,
  perm = 1000,
  reference,
  scale_reference = TRUE,
  plot = FALSE,
  scale_mrna = TRUE,
  group_list = NULL,
  platform = "affymetrix",
  absolute.mode = FALSE,
  abs.method = "sig.score",
  ...
)

Arguments

eset

Gene expression matrix with HGNC symbols as row names.

project

Optional project name. Default is `NULL`.

method

Deconvolution method. See [tme_deconvolution_methods].

arrays

Logical: microarray-optimized mode. Default is `FALSE`.

tumor

Logical: tumor-optimized mode (EPIC). Default is `TRUE`.

perm

Permutations (CIBERSORT/SVR). Default is 1000.

reference

Custom reference matrix (SVR/lsei).

scale_reference

Logical: scale reference (SVR/lsei).

plot

Logical: generate plots (IPS). Default is `FALSE`.

scale_mrna

Logical: mRNA correction (quanTIseq/EPIC).

group_list

Cancer types for TIMER (vector).

platform

Platform for ESTIMATE. Default is `"affymetrix"`.

absolute.mode

Logical: absolute mode (CIBERSORT/SVR). Default is `FALSE`.

abs.method

Absolute mode method. Default is `"sig.score"`.

...

Additional arguments passed to method.

Value

Tibble with cell fractions and `ID` column.

References

  1. Newman et al. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods.

  2. Vegesna et al. (2013). Inferring tumour purity and stromal/immune cell admixture. Nature Communications.

  3. Finotello et al. (2019). Molecular and pharmacological modulators of the tumor immune contexture. Genome Medicine.

  4. Li et al. (2016). Comprehensive analyses of tumor immunity. Genome Biology.

  5. Charoentong et al. (2017). Pan-cancer Immunogenomic Analyses. Cell Reports.

  6. Becht et al. (2016). Estimating population abundance of tissue-infiltrating immune cells. Genome Biology.

  7. Aran et al. (2017). xCell: digitally portraying tissue cellular heterogeneity. Genome Biology.

  8. Racle et al. (2017). Simultaneous enumeration of cancer and immune cell types. ELife.

Author

Dongqiang Zeng, Rongfang Shen

Examples

lm22 <- load_data("lm22")
common_genes <- rownames(lm22)[1:500]
sim_eset <- as.data.frame(matrix(
  rnorm(length(common_genes) * 5, mean = 5, sd = 2),
  nrow = length(common_genes), ncol = 5
))
rownames(sim_eset) <- common_genes
colnames(sim_eset) <- paste0("Sample", 1:5)
res <- deconvo_tme(eset = sim_eset, method = "cibersort", perm = 10)
#> Warning: Data values appear small (< 50).
#> ℹ Input should be in TPM/FPKM scale, not log-transformed
#> ℹ Running CIBERSORT