LFCseq: a nonparametric approach for differential expression analysis of RNA-seq data.

Content Provider	Europe PMC
Author	Lin, Bingqing Zhang, Li-Feng Chen, Xin
Copyright Year	2014
Abstract	BackgroundWith the advances in high-throughput DNA sequencing technologies, RNA-seq has rapidly emerged as a powerful tool for the quantitative analysis of gene expression and transcript variant discovery. In comparative experiments, differential expression analysis is commonly performed on RNA-seq data to identify genes/features that are differentially expressed between biological conditions. Most existing statistical methods for differential expression analysis are parametric and assume either Poisson distribution or negative binomial distribution on gene read counts. However, violation of distributional assumptions or a poor estimation of parameters often leads to unreliable results.ResultsIn this paper, we introduce a new nonparametric approach called LFCseq that uses log fold changes as a differential expression test statistic. To test each gene for differential expression, LFCseq estimates a null probability distribution of count changes from a selected set of genes with similar expression strength. In contrast, the nonparametric NOISeq approach relies on a null distribution estimated from all genes within an experimental condition regardless of their expression levels.ConclusionThrough extensive simulation study and RNA-seq real data analysis, we demonstrate that the proposed approach could well rank the differentially expressed genes ahead of non-differentially expressed genes, thereby achieving a much improved overall performance for differential expression analysis.
Journal	BMC Genomics
Volume Number	15 Suppl 10
PubMed Central reference number	PMC4304217
Issue Number	Suppl 10
PubMed reference number	25560842
e-ISSN	14712164
DOI	10.1186/1471-2164-15-s10-s7
Language	English
Publisher	BioMed Central
Publisher Date	2014-12-12
Access Restriction	Open
Rights License	This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Copyright © 2014 Lin et al.; licensee BioMed Central Ltd.
Subject Keyword	Differential expression Nonparametric RNA-seq
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics