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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Published on: September 18, 2021

Higher order asymptotics for negative binomial regression inferences from RNA-sequencing data.

Yanming Di1, Sarah C Emerson, Daniel W Schafer

  • 1Department of Statistics, Oregon State University, Corvallis, OR 97330,USA. diy@stat.oregonstate.edu

Statistical Applications in Genetics and Molecular Biology
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a higher-order asymptotic (HOA) adjustment for likelihood ratio tests in RNA sequencing (RNA-Seq) gene expression analysis. The HOA test accurately controls error rates and maintains statistical power, even with small sample sizes typical in RNA-Seq studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • RNA sequencing (RNA-Seq) is a key technology for transcriptome characterization and gene expression quantification.
  • The negative binomial (NB) distribution models RNA-Seq read counts, forming the basis for statistical analyses.
  • Existing NB exact tests are limited to two-group comparisons, lacking applicability to regression models.

Purpose of the Study:

  • To evaluate the adequacy of large-sample tests for small RNA-Seq sample sizes.
  • To introduce and assess a higher-order asymptotic (HOA) adjustment for likelihood ratio tests in NB regression.
  • To compare the performance of HOA-adjusted tests against exact tests and unadjusted likelihood ratio tests.

Main Methods:

  • Utilized simulation studies to examine type I error rates and statistical power.
  • Compared the HOA-adjusted likelihood ratio test with exact negative binomial tests.
  • Investigated the performance of the HOA adjustment in negative binomial regression settings.

Main Results:

  • The HOA-adjusted likelihood ratio test closely matches the exact test where applicable.
  • The HOA test demonstrates controlled type I error rates in simulated regression scenarios.
  • Statistical power of the likelihood ratio test is not diminished by the HOA adjustment.

Conclusions:

  • The HOA adjustment provides an accurate and reliable method for likelihood ratio tests in RNA-Seq analysis, especially with small sample sizes.
  • The HOA test offers advantages over unadjusted tests by not requiring ad hoc library size adjustments.
  • This work clarifies the performance of existing tests and validates the utility of the HOA adjustment for gene expression studies.