Alexey Sergushichev | Search bioRxiv

bioRxiv2016

An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation

Gene set enrichment analysis (GSEA) is an ubiquitously used tool for evaluating pathway enrichment in transcriptional data. Typical experimental design consists in comparing two conditions with several replicates using a differential gene expression test followed by preranked GSEA performed against a collection of hundreds and thousands of pathways. However, the reference implementation of this method cannot accurately estimate small P-values, which significantly limits its sensitivity due to multiple hypotheses correction procedure. Here we present FGSEA (Fast Gene Set Enrichment Analysis) method that is able to estimate arbitrarily low GSEA P-values with a high accuracy in a matter of minutes or even seconds. To confirm the accuracy of the method, we also developed an exact algorithm for GSEA P-values calculation for integer gene-level statistics. Using the exact algorithm as a reference we show that FGSEA is able to routinely estimate P-values up to 10-100 with a small and predictable estimation error. We systematically evaluate FGSEA on a collection of 605 datasets and show that FGSEA recovers much more statistically significant pathways compared to other implementations. FGSEA is open source and available as an R package in Bioconductor (http://bioconductor.org/packages/fgsea/) and on GitHub (https://github.com/ctlab/fgsea/).

Bioinformatics↗

bioRxiv2015

Model based normalization improves differential expression calling in low-depth RNA-seq

RNA-seq is a powerful tool for gene expression profiling and differential expression analysis. Its power depends on sequencing depth which limits its high-throughput potential, with 10-15 million reads considered as optimal balance between quality of differential expression calling and cost per sample. We observed, however, that some statistical features of the data, e.g. gene count distribution, are preserved well below 10-15M reads, and found that they improve differential expression analysis at low sequencing depths when distribution statistics is estimated by pooling individual samples to a combined higher-depth library. Using a novel gene-by-gene scaling technique, based on the fact that gene counts obey Pareto-like distribution1, we re-normalize samples towards bigger sequencing depth and show that this leads to significant improvement in differential expression calling, with only a marginal increase in false positive calls. This makes differential expression calling from 3-4M reads comparable to 10-15M reads, improving high-throughput of RNA-sequencing 3-4 fold.

Genomics↗