Online FDR Control for RNAseq Data

TL;DR

This paper introduces an online FDR control method for sequential RNAseq experiments, ensuring false discovery rate control over time, which is more reliable than traditional offline methods.

Contribution

It applies online hypothesis testing methodology to RNAseq data analysis, providing a principled approach to control FDR in sequential experiments.

Findings

Online FDR algorithms guarantee FDR control over time.

Compared to offline methods, online approaches maintain power in simulations.

The onlineFDR package is publicly available for implementation.

Abstract

Motivation: While the analysis of a single RNA sequencing (RNAseq) dataset has been well described in the literature, modern research workflows often have additional complexity in that related RNAseq experiments are performed sequentially over time. The simplest and most widely used analysis strategy ignores the temporal aspects and analyses each dataset separately. However, this can lead to substantial inflation of the overall false discovery rate (FDR). We propose applying recently developed methodology for online hypothesis testing to analyse sequential RNAseq experiments in a principled way, guaranteeing FDR control at all times while never changing past decisions. Results: We show that standard offline approaches have variable control of FDR of related RNAseq experiments over time and a naively composed approach may improperly change historic decisions. We demonstrate that the online FDR algorithms are a principled way to guarantee control of FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to offline approaches. Availability and Implementation: The onlineFDR package is freely available at http: //www.bioconductor.org/packages/onlineFDR. Additional code used for the simulation studies can be found at https://github.com/latlio/onlinefdr_rnaseq_simulation