A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets

Code

• github.com/ggloor/CoDaSeq
  OfficialIn papernone★ 0

Abstract

Motivation: P values derived from the null hypothesis significance testing framework are strongly affected by sample size, and are known to be irreproducible in underpowered studies, yet no suitable replacement has been proposed. Results: Here we present implementations of non-parametric standardized median effect size estimates, dNEF, for high-throughput sequencing datasets. Case studies are shown for transcriptome and tag-sequencing datasets. The dNEF measure is shown to be more reproducible and robust than P values and requires sample sizes as small as 3 to reproducibly identify differentially abundant features. Availability: Source code and binaries freely available at: https://bioconductor.org/packages/ALDEx2.html , omicplotR, and https://github.com/ggloor/CoDaSeq .

Tasks

Reproductions