Title: A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets,
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 repro- ducible 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:
this https URL, omicplotR, and
this https URL.