Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

David M Rocke, Luyao Ruan, Yilun Zhang, J. Jared Gossett, Blythe Durbin-Johnson, Sharon Aviran
doi: http://dx.doi.org/10.1101/020784

Motivation: An important property of a valid method for testing for differential expression is that the false positive rate should at least roughly correspond to the p-value cutoff, so that if 10,000 genes are tested at a p-value cutoff of 10−4, and if all the null hypotheses are true, then there should be only about 1 gene declared to be significantly differentially expressed. We tested this by resampling from existing RNA-Seq data sets and also by matched negative binomial simulations. Results: Methods we examined, which rely strongly on a negative binomial model, such as edgeR, DESeq, and DESeq2, show large numbers of false positives in both the resampled real-data case and in the simulated negative binomial case. This also occurs with a negative binomial generalized linear model function in R. Methods that use only the variance function, such as limma-voom, do not show excessive false positives, as is also the case with a variance stabilizing transformation followed by linear model analysis with limma. The excess false positives are likely caused by apparently small biases in estimation of negative binomial dispersion and, perhaps surprisingly, occur mostly when the mean and/or the dis-persion is high, rather than for low-count genes.

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One thought on “Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

  1. Pingback: Most viewed on Haldane’s Sieve: June 2015 | Haldane's Sieve

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