NBumiFitModel {M3Drop} | R Documentation

## Fit Depth-Adjusted Negative Binomial Model

### Description

Fits means and dispersions for depth-adjusted or basic negative binomial models to a read/UMI count matrix.

### Usage

    	NBumiFitModel(counts)
    	NBumiFitBasicModel(counts)

### Arguments

`counts` | a numeric matrix of raw UMI counts, columns = samples, rows = genes.

### Details

`NBumiFitModel` Fits a depth-adjusted negative binomial model for each expression observation. Each expression is modelled using a negative binomial distribution with mean equal to

t_i*t_j/T,

where t_i is the total counts for sample i, t_j is the total counts for gene j and T is the total counts. Dispersions (R 'siz'e parameter) are fit such that:

var_j(counts_ij-mu_ij) = sum(mu_ij+mu_ij^2/size_j).

Cases where genes exhibit poissonian behavior (size->infinity) are assigned a size of 10^10.

`NBumiFitBasicModel` Fits a basic negative binomial model for each expression observation. Each expression is modelled using a negative binomial distribution with mean equal to

t_j/n,

where t_j is the total counts for gene j and n is the number of cells. Dispersions (R 'size' parameter) are fit such that:

var_j(counts_ij) = sum(mu_j+mu_j^2/size_j).

Cases where genes exhibit poissonian behavior (size->infinity) are assigned a size of 10^10.

### Value

A named list of:

mus

  = a genes by samples matrix of means for the observation-specific negative binomial

sizes

  = a vector of dispersions for each gene.

vals

  = named list of summary statistics of the original count matrix:

tis

  = total molecules per cell,

tjs

  = total molecules per gene,

dis

  = total dropouts per cell,

djs

  = total dropouts per genes,

total

  = total molecules in dataset,

nc

  = number of cells,

ng

  = number of genes

### Examples

    	library(M3DExampleData)
    	counts <- NBumiConvertData(Mmus_example_list$data)
    	fit <- NBumiFitModel(counts);

[Package M3Drop version 1.18.0 Index]