deconvolute_quantiseq.default: Use quanTIseq to Deconvolute a Gene Expression Matrix

In IOBR: Immune Oncology Biological Research

Use quanTIseq to Deconvolute a Gene Expression Matrix

Description

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.

Author(s)

Finotello F, et al. (adapted for IOBR)

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.

Examples

## Not run: 
quantiseq_data <- load_data("quantiseq_data")
if (!is.null(quantiseq_data)) {
  common_genes <- rownames(quantiseq_data$TIL10_signature)
  tpm_matrix <- as.data.frame(matrix(
    abs(rnorm(length(common_genes) * 2, mean = 5, sd = 2)),
    nrow = length(common_genes), ncol = 2
  ))
  rownames(tpm_matrix) <- common_genes
  colnames(tpm_matrix) <- paste0("Sample", 1:2)
  results <- deconvolute_quantiseq.default(mix.mat = tpm_matrix)
  if (!is.null(results)) head(results)
}

## End(Not run)

IOBR documentation built on May 30, 2026, 5:07 p.m.