title: multiDE: a dimension reduced model based statistical method for differential expression analysis using RNA-sequencing data with multiple treatment conditions.
abstract: BACKGROUND
  The growing complexity of biological experiment design based on high-throughput RNA sequencing (RNA-seq) is calling for more accommodative statistical tools. We focus on differential expression (DE) analysis using RNA-seq data in the presence of multiple treatment conditions.
  
  
  RESULTS
  We propose a novel method, multiDE, for facilitating DE analysis using RNA-seq read count data with multiple treatment conditions. The read count is assumed to follow a log-linear model incorporating two factors (i.e., condition and gene), where an interaction term is used to quantify the association between gene and condition. The number of the degrees of freedom is reduced to one through the first order decomposition of the interaction, leading to a dramatically power improvement in testing DE genes when the number of conditions is greater than two. In our simulation situations, multiDE outperformed the benchmark methods (i.e. edgeR and DESeq2) even if the underlying model was severely misspecified, and the power gain was increasing in the number of conditions. In the application to two real datasets, multiDE identified more biologically meaningful DE genes than the benchmark methods. An R package implementing multiDE is available publicly at http://homepage.fudan.edu.cn/zhangh/softwares/multiDE .
  
  
  CONCLUSIONS
  When the number of conditions is two, multiDE performs comparably with the benchmark methods. When the number of conditions is greater than two, multiDE outperforms the benchmark methods.
authors.affiliation: Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, People's Republic of China.
authors.forename: Guangliang
authors.initials: G
authors.lastname: Kang
authors.affiliation: Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, People's Republic of China.
authors.forename: Li
authors.initials: L
authors.lastname: Du
authors.affiliation: Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, People's Republic of China. zhanghfd@fudan.edu.cn.|State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, People's Republic of China. zhanghfd@fudan.edu.cn.
authors.forename: Hong
authors.initials: H
authors.lastname: Zhang
mesh_terms: D053595:Embryonic Stem Cells / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016014:Linear Models; D011565:Psoriasis / Q000188:drug therapy / Q000235:genetics; D017423:Sequence Analysis, RNA; D012984:Software
keywords: Differential expression; Multiple conditions; RNA-seq