getDatasetZTNB: Estimate feature-wise parameters according to a ZTNB...

In statOmics/zingeR: Zero-Inflated Negative binomial Gene Expression in R

Description

This function fits a zero-truncated negative binomial distribution to the positive counts for each feature (row). By default, the zero-truncated negative binomial distribution from the gamlss package is used for this function.

Usage

getDatasetZTNB(counts, design, drop.extreme.dispersion = FALSE,
  offset = NULL)

Arguments

counts	A numeric matrix containing gene expression counts. Note that every gene in this matrix must have at least $p+1$ positive counts, with $p$ the number of columns in the design matrix.
design	The design of the experiments with rows corresponding to samples and columns corresponding to coefficients.
drop.extreme.dispersion	Either a numeric value between $0$ and $1$, stating the proportion of genes with extreme (high) dispersions to remove for simulation, or FALSE (default), if no dispersions should be removed for the analysis.
offset	The offset to use (typically the sequencing depth) when estimating gene-wise means and dispersions in the zero-truncated negative binomial model. These parameters will be used as a basis for the simulation.

See Also

NBsimSingleCell

Examples

data(islamEset,package="zingeR")
islam=exprs(islamEset)[1:2000,]
design=model.matrix(~pData(islamEset)[,1])
params = getDatasetZTNB(counts=islam, design=design)

statOmics/zingeR documentation built on May 20, 2019, 6:48 p.m.