MGstatistic {debCAM} | R Documentation

## Statistics for ranking marker genes

### Description

This function computes One-Versus-Everyone Fold Change (OVE-FC) from subpopulation-specific expression profiles. Bootstrapping is optional.

### Usage

    MGstatistic(data, A = NULL, boot.alpha = NULL, nboot = 1000,
      cores = NULL)

### Arguments

`data` | A data set that will be internally coerced into a matrix. Each row is a gene and each column is a sample. Data should be in non-log linear space with non-negative numerical values (i.e. >= 0). Missing values are not supported. All-zero rows will be removed internally.
`A` | When data are mixture expression profiles, A is estimated proportion matrix or prior proportion matrix. When data are pure expression profiles, A is a phenotype vector to indicate which subpopulation each sample belongs to.
`boot.alpha` | Alpha for bootstrapped OVE-FC confidence interval. The default is 0.05.
`nboot` | The number of boots.
`cores` | The number of system cores for parallel computing. If not provided, the default back-end is used.

### Details

This function calculates OVE-FC and bootstrapped OVE-FC which can be used to identify markers from all genes.

### Value

A data frame containing the following components:

`idx` | Numbers or phenotypes indicating which subpopulation each gene could be a marker for. If A is a proportion matrix without column name, numbers are returned. Otherwise, phenotypes.
`OVE.FC` | One-versus-Everyone fold change (OVE-FC)
`OVE.FC.alpha` | lower confidence bound of bootstrapped OVE-FC at alpha level.

### Examples

    #data are mixture expression profiles, A is proportion matrix
    data(ratMix3)
    MGstat <- MGstatistic(ratMix3$X, ratMix3$A)
    ## Not run:
    MGstat <- MGstatistic(ratMix3$X, ratMix3$A, boot.alpha = 0.05) #enable boot

    ## End(Not run)

    #data are pure expression profiles without replicates
    MGstat <- MGstatistic(ratMix3$S) #boot is not applicable
    ## Not run:
    #data are pure expression profiles with phenotypes
    S <- matrix(rgamma(3000,0.1,0.1), 1000, 3)
    S <- S[, c(1,1,1,2,2,2,3,3,3,3)] + rnorm(1000*10, 0, 0.5)
    MGstat <- MGstatistic(S, c(1,1,1,2,2,2,3,3,3,3), boot.alpha = 0.05)

    ## End(Not run)

[Package debCAM version 1.12.0 Index]