estCellPercent.svmdecon: SVMDECON deconvolution

In ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells

SVMDECON deconvolution

Description

Use SVMDECON to estimate the cell count percentage Performs considerably worse in deconvolution than DCQ

cellEst <- estCellPercent.svmdecon(refExpr, geneExpr)

Usage

estCellPercent.svmdecon(
  refExpr,
  geneExpr,
  marker_set = NULL,
  useOldVersion = F,
  progressBar = T
)

Arguments

refExpr	a data frame representing immune cell expression profiles. Each row represents an expression of a gene, and each column represents a different immune cell type. colnames contains the name of each immune cell type and the rownames includes the genes' symbol. The names of each immune cell type and the symbol of each gene should be unique. Any gene with missing expression values must be excluded.
geneExpr	a data frame representing RNA-seq or microarray gene-expression profiles of a given complex tissue. Each row represents an expression of a gene, and each column represents a different experimental sample. colnames contain the name of each sample and rownames includes the genes' symbol. The name of each individual sample and the symbol of each gene should be unique. Any gene with missing expression values should be excluded.
marker_set	data frames of one column, that includes a preselected list of genes that likely discriminate well between the immune-cell types given in the reference data. (DEFAULT: NULL, i.e. one for each gene in the refExpr)
useOldVersion	Set the TRUE to 2^ the data (DEFAULT: FALSE)
progressBar	Set to TRUE to show a progress bar (DEFAULT: TRUE)

Value

A matrix with cell type estimates for each samples #This toy example library(ADAPTS) fullLM22 <- ADAPTS::LM22[1:30, 1:4] smallLM22 <- fullLM22[1:25,]

cellEst <- estCellPercent.svmdecon(refExpr=smallLM22, geneExpr=fullLM22)

ADAPTS documentation built on Sept. 14, 2022, 9:05 a.m.