Dataset Information

Global FDR control across multiple RNAseq experiments.

ABSTRACT:

Motivation

While classical approaches for controlling the false discovery rate (FDR) of RNA sequencing (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 data are available at Bioinformatics online.

SUBMITTER: Liou L

PROVIDER: S-EPMC9805573 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Global FDR control across multiple RNAseq experiments.

Liou Lathan L Hornburg Milena M Robertson David S DS

Bioinformatics (Oxford, England) 20230101 1

<h4>Motivation</h4>While classical approaches for controlling the false discovery rate (FDR) of RNA sequencing (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 rec ...[more]

PMID: 36326442

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Project description:BackgroundIn microbiome studies, it is important to detect taxa which are associated with pathological outcomes at the lowest definable taxonomic rank, such as genus or species. Traditionally, taxa at the target rank are tested for individual association, followed by the Benjamini-Hochberg (BH) procedure to control for false discovery rate (FDR). However, this approach neglects the dependence structure among taxa and may lead to conservative results. The taxonomic tree of microbiome data represents alignment from phylum to species rank and characterizes evolutionary relationships across microbial taxa. Taxa that are closer on the tree usually have similar responses to the exposure (environment). The statistical power in microbial association tests can be enhanced by efficiently employing the prior evolutionary information via the taxonomic tree.MethodsWe propose a two-stage microbial association mapping framework (massMap) which uses grouping information from the taxonomic tree to strengthen statistical power in association tests at the target rank. massMap first screens the association of taxonomic groups at a pre-selected higher taxonomic rank using a powerful microbial group test OMiAT. The method then proceeds to test the association for each candidate taxon at the target rank within the significant taxonomic groups identified in the first stage. Hierarchical BH (HBH) and selected subset testing (SST) procedures are evaluated to control the FDR for the two-stage structured tests.ResultsOur simulations show that massMap incorporating OMiAT and the advanced FDR controlling methodologies largely alleviates the multiplicity issue. It is statistically more powerful than the traditional association mapping directly at the target rank while controlling the FDR at desired levels under most scenarios. In our real data analyses, massMap detects more or the same amount of associated species with smaller adjusted p values compared to the traditional method, which further illustrates the efficiency of the proposed framework. The R package of massMap is publicly available at https://sites.google.com/site/huilinli09/software and https://github.com/JiyuanHu/ .ConclusionsmassMap is a novel microbial association mapping framework and achieves additional efficiency by utilizing the intrinsic taxonomic structure of microbiome data.

Dataset Information

Global FDR control across multiple RNAseq experiments.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Global FDR control across multiple RNAseq experiments.

Similar Datasets

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