dcq {ComICS}

DCQ - Digital Cell Quantifier

Description

DCQ combines genome-wide gene expression data with an immune cell-type reference data to infer changes in the quantities immune cell subpopulations.

Usage

dcq(reference_data, mix_data, marker_set, alpha_used=0.05,
 lambda_min=0.2, number_of_repeats=3, precent_of_data=1.0)

Arguments

reference_data

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.

mix_data

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.

alpha_used, lambda_min

parameters of the L1 and L2 regularization. It is generally recommended to leave the default value. For more information about this parameter, see the glmnet package.

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.

precent_of_data

in order to run the analysis over all the cell types use 1.0. For bootstrap purposes, you can use part of the data (e.g, 0.5).

Value

a list that contains two matrices

average

a matrix that contains the average relative quantities for each cell type in everytest sample.

stdev

a matrix that contains the standard deviations over all repeats for each cell types in each test sample.

References

Altboum Z, Steuerman Y, David E, Barnett-Itzhaki Z, Valadarsky L, Keren-Shaul H, et al. Digital cell quantification identifies global immune cell dynamics during influenza infection. Mol Syst Biol. 2014;10: 720. doi:10.1002/msb.134947

Examples

data(commons)
data(dcqEx)
results <- dcq(reference_data=immgen_dat, mix_data=lung_time_series_dat, marker_set=DCQ_mar)