Multiple hypothesis testing and RNA-seq differential expression analysis accounting for dependence and relevant covariates

Nguyen, Yet

doi:10.31274/etd-180810-6056

Cited by 1 publication

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References 36 publications

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“…In addition to the lack of theoretical guarantees on the power to detect DE genes using time‐course RNA‐seq data, the FDR control of existing methods is not well studied. Simulation and empirical studies like Kvam et al (), Law et al (), Chu et al (), and Van den Berge et al () show that both DESeq and edgeR are conservative in some cases while liberal in others for traditional RNA‐seq analysis as well as for analyzing time‐course RNA‐seq data (Sun et al , ; Nguyen, ). Our comprehensive simulations support the same conclusions.…”

Section: Introductionmentioning

confidence: 99%

Large scale maximum average power multiple inference on time‐course count data with application to RNA‐seq analysis

et al. 2019

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Experiments that longitudinally collect RNA sequencing (RNA‐seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large‐scale time‐course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.

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