No abstract

Motivation While classical approaches for controlling the false discovery rate (FDR) of RNAseq experiments have been well described, modern research workflows and growing databases enable a new paradigm of controlling the FDR globally across RNAseq experiments in the past, present, and future. The simplest analysis strategy that analyses each RNAseq experiment separately and applies an FDR correction method can lead to inflation of the overall FDR. We propose applying recently developed methodology for online multiple hypothesis testing to control the global FDR in a principled way across multiple RNAseq experiments. Results We show that repeated application of classical repeated offline approaches has variable control of global FDR of RNAseq experiments over time. We demonstrate that the online FDR algorithms are a principled way to control FDR. Furthermore, in certain simulation scenarios, we observe empirically that online approaches have comparable power to repeated 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 Supplementary Information Supplementary Appendix is available in Bioinformatics online.

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.