A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq data

dc.contributor.authorLiu, Kefei
dc.contributor.authorYe, Jieping
dc.contributor.authorYang, Yang
dc.contributor.authorShen, Li
dc.contributor.authorJiang, Hui
dc.contributor.departmentRadiology and Imaging Sciences, School of Medicineen_US
dc.date.accessioned2018-09-20T15:03:23Z
dc.date.available2018-09-20T15:03:23Z
dc.date.issued2018-01
dc.description.abstractThe RNA-sequencing (RNA-seq) is becoming increasingly popular for quantifying gene expression levels. Since the RNA-seq measurements are relative in nature, between-sample normalization of counts is an essential step in differential expression (DE) analysis. The normalization of existing DE detection algorithms is ad hoc and performed once for all prior to DE detection, which may be suboptimal since ideally normalization should be based on non-DE genes only and thus coupled with DE detection. We propose a unified statistical model for joint normalization and DE detection of log-transformed RNA-seq data. Sample-specific normalization factors are modeled as unknown parameters in the gene-wise linear models and jointly estimated with the regression coefficients. By imposing sparsity-inducing L1 penalty (or mixed L1/L2 penalty for multiple treatment conditions) on the regression coefficients, we formulate the problem as a penalized least-squares regression problem and apply the augmented lagrangian method to solve it. Simulation studies show that the proposed model and algorithms perform better than or comparably to existing methods in terms of detection power and false-positive rate. The performance gain increases with increasingly larger sample size or higher signal to noise ratio, and is more significant when a large proportion of genes are differentially expressed in an asymmetric manner.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationLiu, K., Ye, J., Yang, Y., Shen, L., & Jiang, H. (2018). A Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. http://dx.doi.org/10.1109/TCBB.2018.2790918en_US
dc.identifier.urihttps://hdl.handle.net/1805/17360
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/TCBB.2018.2790918en_US
dc.relation.journalIEEE/ACM Transactions on Computational Biology and Bioinformaticsen_US
dc.rightsIUPUI Open Access Policyen_US
dc.sourceAuthoren_US
dc.subjectRNA-seqen_US
dc.subjectdifferential expression analysisen_US
dc.subjectnormalizationen_US
dc.titleA Unified Model for Joint Normalization and Differential Gene Expression Detection in RNA-Seq dataen_US
dc.typeArticleen_US
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