% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deconvo_tme.R
\name{deconvo_tme}
\alias{deconvo_tme}
\title{Main TME Deconvolution Function}
\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{
\item{eset}{Gene expression matrix with HGNC symbols as row names.}

\item{project}{Optional project name. Default is `NULL`.}

\item{method}{Deconvolution method. See [tme_deconvolution_methods].}

\item{arrays}{Logical: microarray-optimized mode. Default is `FALSE`.}

\item{tumor}{Logical: tumor-optimized mode (EPIC). Default is `TRUE`.}

\item{perm}{Permutations (CIBERSORT/SVR). Default is 1000.}

\item{reference}{Custom reference matrix (SVR/lsei).}

\item{scale_reference}{Logical: scale reference (SVR/lsei).}

\item{plot}{Logical: generate plots (IPS). Default is `FALSE`.}

\item{scale_mrna}{Logical: mRNA correction (quanTIseq/EPIC).}

\item{group_list}{Cancer types for TIMER (vector).}

\item{platform}{Platform for ESTIMATE. Default is `"affymetrix"`.}

\item{absolute.mode}{Logical: absolute mode (CIBERSORT/SVR).
Default is `FALSE`.}

\item{abs.method}{Absolute mode method. Default is `"sig.score"`.}

\item{...}{Additional arguments passed to method.}
}
\value{
Tibble with cell fractions and `ID` column.
}
\description{
Unified interface for multiple TME deconvolution methods.
}
\examples{
mcp_genes <- load_data("mcp_genes")
if (!is.null(mcp_genes)) {
  set.seed(123)
  sim_eset <- matrix(rnorm(nrow(mcp_genes) * 3), nrow(mcp_genes), 3)
  rownames(sim_eset) <- mcp_genes$`HUGO symbols`
  colnames(sim_eset) <- paste0("Sample", 1:3)
  
  # Run deconvolution
  result <- deconvo_tme(eset = sim_eset, method = "mcpcounter")
  if (!is.null(result)) head(result)
}
}
\references{
\enumerate{
  \item Newman et al. (2015). Robust enumeration of cell subsets from tissue
    expression profiles. Nature Methods.
  \item Vegesna et al. (2013). Inferring tumour purity and stromal/immune
    cell admixture. Nature Communications.
  \item Finotello et al. (2019). Molecular and pharmacological modulators of
    the tumor immune contexture. Genome Medicine.
  \item Li et al. (2016). Comprehensive analyses of tumor immunity.
    Genome Biology.
  \item Charoentong et al. (2017). Pan-cancer Immunogenomic Analyses.
    Cell Reports.
  \item Becht et al. (2016). Estimating population abundance of tissue-infiltrating
    immune cells. Genome Biology.
  \item Aran et al. (2017). xCell: digitally portraying tissue cellular
    heterogeneity. Genome Biology.
  \item Racle et al. (2017). Simultaneous enumeration of cancer and immune
    cell types. ELife.
}
}
\author{
Dongqiang Zeng, Rongfang Shen
}
