NBumiCoexpression {M3Drop} | R Documentation

## Variance-based Feature Selection

### Description

Ranks genes by residual dispersion from mean-dispersion power-law relationship.

### Usage

    	NBumiCoexpression(counts, fit, gene_list=NULL, method=c("both", "on", "off"))

### Arguments

`counts` | raw count matrix (e.g. from NBumiConvertData).
`fit` | output from NBumiFitModel or NBumiFitBasicModel.
`gene_list` | set of gene names to test coexpression of.
`method` | type of coexpression to test (see: Details).

### Details

Tests for co-expression using the normal approximation of a binomial test. Co-expression is defined according to the method argument as follows:

on

  two genes are both >0 in more cells than expected.

off

  two genes are both 0 in more cells than expected.

both

  two genes are either both >0 or both 0 in more cells than expected.

In all cases the null expectation is calculated using the fit library-size adjusted negative binomial model. This remove spurious co-expression due to cells with differing numbers of detected genes.

### Value

a matrix of Z-scores for each pair of genes in the provided gene list.

### Examples

    	library(M3DExampleData)
    	counts <- NBumiConvertData(Mmus_example_list$data)
    	fit <- NBumiFitModel(counts);
    	genes <- c("Sox2", "Eomes", "Zscan4d", "Obox1", "Obox3")
    	co <- NBumiCoexpression(counts, fit, genes, method="both");
    	on <- NBumiCoexpression(counts, fit, genes, method="on");
    	off <- NBumiCoexpression(counts, fit, genes, method="off");

[Package M3Drop version 1.18.0 Index]