Topconfects: A package for confident effect sizes in differential expression analysis provides a more biologically useful ranked gene list

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Abstract

Differential gene expression analysis may discover a set of genes too large to easily investigate, so a means of ranking genes by biological interest level is desired. p values are frequently abused for this purpose. As an alternative, we propose a method of ranking by confidence bounds on the log fold change, based on the previously developed TREAT test. These confidence bounds provide guaranteed false discovery rate and false coverage-statement rate control. When applied to a breast cancer dataset, the top-ranked genes by Topconfects emphasize markedly different biological processes compared to the top-ranked genes by p value.

Original languageEnglish
Article number67
Number of pages12
JournalGenome Biology
Volume20
Issue number1
DOIs
Publication statusPublished - 28 Mar 2019

Keywords

  • Confidence interval
  • Differential expression analysis
  • False coverage-statement rate
  • False discovery rate
  • RNA-Seq
  • TREAT

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