R: DCQ - Digital Cell Quantifier

dcq {ComICS} | R Documentation

## 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)

[Package ComICS version 1.0.4 Index]