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

Abstract

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.

Keywords

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