Error estimates for the analysis of differential expression from RNA-seq count data
- Published
- Accepted
- Subject Areas
- Bioinformatics
- Keywords
- RNA-seq, differential expression analysis, false discovery rates
- Copyright
- © 2014 Burden et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Error estimates for the analysis of differential expression from RNA-seq count data. PeerJ PrePrints 2:e400v3 https://doi.org/10.7287/peerj.preprints.400v3
Abstract
A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis data and to inaccurate estimates of false discovery rates (FDRs). This can lead to an inaccurate measure of significance and loss of power to detect differential expression.
We use synthetic and real biological data to assess the ability of several available R packages to accurately estimate FDRs. The packages surveyed are based on statistical models of overdispersed Poisson data and include edgeR, DESeq, DESeq2, PoissonSeq and QuasiSeq. Also tested is an add-on package to edgeR and DESeq which we introduce called Polyfit. Polyfit aims to address the problem of a non-uniform null p-value distribution for two-class datasets by adapting the Storey-Tibshirani procedure.
We find the best performing package in the sense that it achieves a low FDR which is accurately estimated over the full range of p-values, albeit with a very slow run time, is the QLSpline implementation of QuasiSeq. This finding holds provided the number of biological replicates in each condition is at least 4. The next best performing packages are edgeR and DESeq2. When the number of biological replicates is sufficiently high, and within a range accessible to multiplexed experimental designs, the Polyfit extension improves the performance DESeq (for approximately 6 or more replicates per condition), making its performance comparable with that of edgeR and DESeq2 in our tests with synthetic data.
Author Comment
This is an updated version with changes to p8 of the main paper and two new figures added to the Supplementary file 1.
Supplemental Information
Supplemental file S1
Figures S1 to S27 cited in the text
R code
R code for the Polyfit software, together with, as an example, the code used for generating Fig. 5, and a data file of mean and overdispersons used for generating the various synthetic data sets used in this paper.