NBumi_FitModel: Fit Depth-Adjusted Negative Binomial Model

In tallulandrews/M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

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 size 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);

tallulandrews/M3Drop documentation built on March 6, 2024, 1:49 a.m.