estCellPercent.DCQ {ADAPTS}

DCQ Deconvolution

Description

Use DCQ to estimate the cell count percentage Requires installation of package 'ComICS' To Do: Also report the standard deviation as a confidence metric

Usage

estCellPercent.DCQ(
  refExpr,
  geneExpr,
  marker_set = NULL,
  number_of_repeats = 10,
  alpha = 0.05,
  lambda = 0.2
)

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)

number_of_repeats

using one repeat will generate only one output model. Using many repeats, DCQ calculates a collection of models, and outputs the average and standard deviation for each predicted relative cell quantity. (DEFAULT: 1)

alpha

The elasticnet mixing parameter, with 0 <= alpha <= 1. alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. (DEFAULT: 0.05)

lambda

A minimum value for the elastic net lambda parameter (DEFAULT: 0.2)

Value

A matrix with cell type estimates for each samples

Examples

#This toy example 
library(ADAPTS)
fullLM22 <- ADAPTS::LM22[1:30, 1:4]
smallLM22 <- fullLM22[1:25,] 

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