ArticlePloS one2017
A Hypothesis Testing Based Method for Normalization and Differential Expression Analysis of RNA-Seq Data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Comparative Transcriptomics Reveals an Extracellular Worm Argonaute as an Ancestral Regulator of LTR Retrotransposons.Genome biology and evolution · 2026Article
- Annotation depth confounds direct comparison of gene expression across species.BMC bioinformatics · 2021Article
- A scaling-free minimum enclosing ball method to detect differentially expressed genes for RNA-seq data.BMC genomics · 2021Article
- A statistical normalization method and differential expression analysis for RNA-seq data between different species.BMC bioinformatics · 2019Article
- A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory.PLoS computational biology · 2019Article
- Analysis of change in microRNA expression profiles of lung cancer A549 cells treated with Radix tetrastigma hemsleyani flavonoids.OncoTargets and therapy · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Next-generation sequencing technologies have made RNA sequencing (RNA-seq) a popular choice for measuring gene expression level. To reduce the noise of gene expression measures and compare them between several conditions or samples, normalization is an essential step to adjust for varying sample sequencing depths and other unwanted technical effects. In this paper, we develop a novel global scaling normalization method by employing the available knowledge of housekeeping genes. We formulate the problem from the hypothesis testing perspective and find an optimal scaling factor that minimizes the deviation between the empirical and the nominal type I error. Applying our approach to various simulation studies and real examples, we demonstrate that it is more accurate and robust than the state-of-the-art alternatives in detecting differentially expression genes.
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