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. 2015 May 15;31(10):1592-8.
doi: 10.1093/bioinformatics/btv023. Epub 2015 Jan 20.

Bias in microRNA Functional Enrichment Analysis

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Free PMC article

Bias in microRNA Functional Enrichment Analysis

Thomas Bleazard et al. Bioinformatics. .
Free PMC article

Abstract

Motivation: Many studies have investigated the differential expression of microRNAs (miRNAs) in disease states and between different treatments, tissues and developmental stages. Given a list of perturbed miRNAs, it is common to predict the shared pathways on which they act. The standard test for functional enrichment typically yields dozens of significantly enriched functional categories, many of which appear frequently in the analysis of apparently unrelated diseases and conditions.

Results: We show that the most commonly used functional enrichment test is inappropriate for the analysis of sets of genes targeted by miRNAs. The hypergeometric distribution used by the standard method consistently results in significant P-values for functional enrichment for targets of randomly selected miRNAs, reflecting an underlying bias in the predicted gene targets of miRNAs as a whole. We developed an algorithm to measure enrichment using an empirical sampling approach, and applied this in a reanalysis of the gene ontology classes of targets of miRNA lists from 44 published studies. The vast majority of the miRNA target sets were not significantly enriched in any functional category after correction for bias. We therefore argue against continued use of the standard functional enrichment method for miRNA targets.

Figures

Fig. 1.
Fig. 1.
Expected and empirical number of predicted targets of randomly selected microRNAs. For an example 39 miRNAs, we calculate the hypergeometric distribution (blue) for the number of expected targets in the GO term ‘ion transport’ (GO:0006811). The empirical distribution (red) represents the predicted targets of random samples of 39 miRNAs. The probability for each 5-gene bin is given according to both distributions

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