Use quanTIseq to Deconvolute a Gene Expression Matrix — deconvolute

Deconvolutes gene expression data to estimate immune cell fractions using the quanTIseq method. Source code from https://github.com/FFinotello/quanTIseq.

Usage

deconvolute_quantiseq.default(
  mix.mat,
  arrays = FALSE,
  signame = "TIL10",
  tumor = FALSE,
  mRNAscale = TRUE,
  method = c("lsei", "hampel", "huber", "bisquare"),
  rmgenes = "unassigned"
)

Arguments

mix.mat: Data frame or matrix. Gene expression matrix with gene symbols on the first column and sample IDs on the first row. Expression data must be on non-log scale (TPM for RNA-seq or expression values for microarrays).
arrays: Logical. Whether expression data are from microarrays. Default is FALSE. If TRUE, the rmgenes parameter is set to "none".
signame: Character. Name of the signature matrix. Currently only "TIL10" is available. Default is "TIL10".
tumor: Logical. Whether expression data are from tumor samples. If TRUE, signature genes with high expression in tumor samples are removed. Default is FALSE.
mRNAscale: Logical. Whether cell fractions must be scaled to account for cell-type-specific mRNA content. Default is TRUE.
method: Character. Deconvolution method: "hampel", "huber", "bisquare" for robust regression, or "lsei" for constrained least squares. Default is "lsei".
rmgenes: Character. Genes to remove: "unassigned" (default), "default", "none", or "path".

Value

Data frame with cell fractions for each sample.

References

F. Finotello, C. Mayer, C. Plattner, G. Laschober, D. Rieder, H. Hackl, A. Krogsdam, W. Posch, D. Wilflingseder, S. Sopper, M. Jsselsteijn, D. Johnsons, Y. Xu, Y. Wang, M. E. Sanders, M. V. Estrada, P. Ericsson-Gonzalez, J. Balko, N. F. de Miranda, Z. Trajanoski. "quanTIseq: quantifying immune contexture of human tumors". bioRxiv 223180. https://doi.org/10.1101/223180.

Author

Finotello F, et al. (adapted for IOBR)

Examples

lm22 <- load_data("lm22")
common_genes <- rownames(lm22)[1:500]
tpm_matrix <- as.data.frame(matrix(
  rnorm(length(common_genes) * 5, mean = 5, sd = 2),
  nrow = length(common_genes), ncol = 5
))
rownames(tpm_matrix) <- common_genes
colnames(tpm_matrix) <- paste0("Sample", 1:5)
results <- deconvolute_quantiseq.default(mix.mat = tpm_matrix)
#> ℹ Running quanTIseq deconvolution module
#> ℹ Gene expression normalization and re-annotation (arrays: FALSE)
#> ℹ Removing 17 noisy genes
#> ℹ Signature genes found in data set: 41/153 (26.8%)
#> ℹ Mixture deconvolution (method: lsei)
#> ✔ Deconvolution successful!