model.nbp.m: Modeling NBP Genewise Dispersion with the Maximum Ajdusted...

In gu-mi/NBGOF: Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data

Description

This function fits an NBP dispersion model where the dispersion parameter is modeled as a linear function of the relative means. See details below. The output of this function will be passed to the main GOF function nb.gof.m.

Usage

model.nbp.m(counts, x, lib.sizes=colSums(counts), method=method)

Arguments

counts	an m-by-n count matrix of non-negative integers. For a typical RNA-Seq experiment, this is the read counts with m genes and n samples.
x	an n-by-p design matrix.
lib.sizes	library sizes of an RNA-Seq experiment. Default is the column sums of the counts matrix.
method	method for estimating dispersions.

Details

Under the NB model, the mean-variance relationship of a single read count satisfies σ_{ij}^2 = μ_{ij} + φ_{ij} μ_{ij}^2. For applying the NBP model to RNA-Seq data, we consider the "log-linear-rel-mean" method assuming a parametric dispersion model φ_{ij} = α_0 + α_1 \log(π_{ij}), where π_{ij} = μ_{ij}/(N_j R_j) is the relative mean frequency after normalization. The parameters (α_0, α_1) in this dispersion model are estimated by maximizing the adjusted profile likelihood. See the estimate.dispersion function in the NBPSeq package for more information.

Value

A list of quantities to be used in the main nb.gof.m function.

Author(s)

Gu Mi <[e-mail]>, Yanming Di, Daniel Schafer

References

Di Y, Schafer DW, Cumbie JS, and Chang JH (2011): "The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq", Statistical Applications in Genetics and Molecular Biology, 10 (1).

See https://github.com/gu-mi/NBGOF/wiki/ for more details.

gu-mi/NBGOF documentation built on Oct. 25, 2020, 3:30 a.m.