title: MetaDiff: differential isoform expression analysis using random-effects meta-regression.
abstract: BACKGROUND
  RNA sequencing (RNA-Seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. A major application of RNA-Seq is to detect differential isoform expression across experimental conditions, which is of great biological interest due to its direct relevance to protein function and disease pathogenesis. Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples. It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates.
  
  
  RESULTS
  In this paper, we present MetaDiff, a random-effects meta-regression model that naturally fits for the above purposes. Through extensive simulations and analysis of an RNA-Seq dataset on human heart failure, we show that the random-effects meta-regression approach is computationally fast, reliable, and can improve the power of differential expression analysis while controlling for false positives due to the effect of covariates or confounding variables. In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables. The source code, compiled JAR package and documentation of MetaDiff are freely available at https://github.com/jiach/MetaDiff.
  
  
  CONCLUSIONS
  Our results indicate that random-effects meta-regression offers a flexible framework for differential expression analysis of isoforms, particularly when gene expression is influenced by other variables.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. jiacheng@mail.med.upenn.edu.
authors.forename: Cheng
authors.initials: C
authors.lastname: Jia
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota School of Public Health, Minneapolis, MN, 55455, USA. wguan@umn.edu.
authors.forename: Weihua
authors.initials: W
authors.lastname: Guan
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. sheepsheep.yang@gmail.com.
authors.forename: Amy
authors.initials: A
authors.lastname: Yang
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. rxiao@mail.med.upenn.edu.
authors.forename: Rui
authors.initials: R
authors.lastname: Xiao
authors.affiliation: Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, 44195, USA. TANGW@ccf.org.
authors.forename: W H Wilson
authors.initials: WH
authors.lastname: Tang
authors.affiliation: Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, 44195, USA. MORAVEC@ccf.org.
authors.forename: Christine S
authors.initials: CS
authors.lastname: Moravec
authors.affiliation: Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. Kenneth.Margulies@uphs.upenn.edu.
authors.forename: Kenneth B
authors.initials: KB
authors.lastname: Margulies
authors.affiliation: Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. thomas.cappola@uphs.upenn.edu.
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Cappola
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. mingyao@mail.med.upenn.edu.
authors.forename: Mingyao
authors.initials: M
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, 44106, USA. cxl791@case.edu.
authors.forename: Chun
authors.initials: C
authors.lastname: Li
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006333:Heart Failure / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D020033:Protein Isoforms; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D020033:Protein Isoforms