find_cna_driven_gene {iGC} | R Documentation

## Perform an integrated analysis of gene expression (GE) and copy number alteration (CNA)

### Description

The function finds CNA-driven differentially expressed gene and returns the corresponding p-value, false discovery rate, and associated statistics. The result includes three tables which collects information for gain-, loss-, and both-driven genes.

### Usage

    find_cna_driven_gene(gene_cna, gene_exp, gain_prop = 0.2, loss_prop = 0.2,
      progress = TRUE, progress_width = 32, parallel = FALSE)

### Arguments

`gene_cna` | Joint CNA table from create_gene_cna.
`gene_exp` | Joint gene expression table from create_gene_exp.
`gain_prop` | Minimum proportion of the gain samples to be consider CNA-gain. Default is 0.2.
`loss_prop` | Minimum proportion of the loss samples to be consider CNA-loss. Default is 0.2.
`progress` | Whether to display a progress bar. By default `TRUE`.
`progress_width` | The text width of the shown progress bar. By default is 48 chars wide.
`parallel` | Enable parallelism by plyr. One has to specify a parallel engine beforehand. See example for more information.

### Details

The gene is considered CNA-gain if the proportion of the sample exhibiting gain exceeds the threshold `gain_prop`, that is, number of samples having `gain_loss` = 1. Reversely, the gene is considered CNA-loss if %samples that `gain_loss` = -1 is below a given threshold `loss_prop`.

When performing the t-test, sample grouping depends on the analysis scenario being either CNA-gain or CNA-loss driven. In CNA-gain driven scenario, two groups, CNA-gain and the other samples, are made. In CNA-loss driven scenario, group CNA-loss and the others are made. Genes that appear in both scenarios will be collected into a third table and excluded from their original tables.

See the vignette for usage of this function by a thorough example.

### Value

List of three data.table objects for CNA-driven scenarios: gain, loss, and both, which can be accessed by names: 'gain_driven', 'loss_driven' and 'both'.

### Examples

    require(data.table)

    ## Create gene_exp and gene_cna manually. The following shows an example
    ## consisting of 3 genes (BRCA2, TP53, and GNPAT) and 5 samples (A to E).

    gene_exp <- data.table(
        GENE = c("BRCA2", "TP53", "GNPAT"),
        A = c(-0.95, 0.89, 0.21), B = c(1.72, -0.05, NA),
        C = c(-1.18, 1.15, 2.47), D = c(-1.24, -0.07, 1.2),
        E = c(1.01, 0.93, 1.54)
    )
    gene_cna <- data.table(
        GENE = c("BRCA2", "TP53", "GNPAT"),
        A = c(1, 1, NA), B = c(-1, -1, 1),
        C = c(1, -1, 1), D = c(1, -1, -1),
        E = c(0, 0, -1)
    )

    ## Find CNA-driven genes

    cna_driven_genes <- find_cna_driven_gene(
        gene_cna, gene_exp, progress=FALSE
    )

    # Gain driven genes
    cna_driven_genes$gain_driven

    # Loss driven genes
    cna_driven_genes$loss_driven

    # Gene shown in both gain and loss records
    cna_driven_genes$both

[Package iGC version 1.18.0 Index]