Goodness-of-fit tests and model diagnostics for negative binomial regression of RNA sequencing data

PloS One
Gu MiDaniel W Schafer

Abstract

This work is about assessing model adequacy for negative binomial (NB) regression, particularly (1) assessing the adequacy of the NB assumption, and (2) assessing the appropriateness of models for NB dispersion parameters. Tools for the first are appropriate for NB regression generally; those for the second are primarily intended for RNA sequencing (RNA-Seq) data analysis. The typically small number of biological samples and large number of genes in RNA-Seq analysis motivate us to address the trade-offs between robustness and statistical power using NB regression models. One widely-used power-saving strategy, for example, is to assume some commonalities of NB dispersion parameters across genes via simple models relating them to mean expression rates, and many such models have been proposed. As RNA-Seq analysis is becoming ever more popular, it is appropriate to make more thorough investigations into power and robustness of the resulting methods, and into practical tools for model assessment. In this article, we propose simulation-based statistical tests and diagnostic graphics to address model adequacy. We provide simulated and real data examples to illustrate that our proposed methods are effective for detecting the misspecifi...Continue Reading

References

Apr 20, 2001·Proceedings of the National Academy of Sciences of the United States of America·V G TusherG Chu
Jul 12, 2002·American Journal of Human Genetics·B V NorthP C Sham
Oct 6, 2004·Genome Biology·Robert C GentlemanJianhua Zhang
Nov 19, 2008·Nature Reviews. Genetics·Zhong WangMichael Snyder
Mar 4, 2010·Genome Biology·Mark D Robinson, Alicia Oshlack
Oct 29, 2010·Genome Biology·Simon Anders, Wolfgang Huber
Oct 30, 2012·Statistical Applications in Genetics and Molecular Biology·Steven P LundGordon K Smyth
Dec 18, 2014·Genome Biology·Michael I LoveSimon Anders

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Methods Mentioned

BETA
RNA-Seq

Software Mentioned

NBPSeq
SAM
NBGOF
DESeq2
QuasiSeq
edgeR Bioconductor
R
DESeq

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