Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias

PLoS Biol. 2019 Nov 12;17(11):e3000481. doi: 10.1371/journal.pbio.3000481. eCollection 2019 Nov.

Abstract

Data normalization is a critical step in RNA sequencing (RNA-seq) analysis, aiming to remove systematic effects from the data to ensure that technical biases have minimal impact on the results. Analyzing numerous RNA-seq datasets, we detected a prevalent sample-specific length effect that leads to a strong association between gene length and fold-change estimates between samples. This stochastic sample-specific effect is not corrected by common normalization methods, including reads per kilobase of transcript length per million reads (RPKM), Trimmed Mean of M values (TMM), relative log expression (RLE), and quantile and upper-quartile normalization. Importantly, we demonstrate that this bias causes recurrent false positive calls by gene-set enrichment analysis (GSEA) methods, thereby leading to frequent functional misinterpretation of the data. Gene sets characterized by markedly short genes (e.g., ribosomal protein genes) or long genes (e.g., extracellular matrix genes) are particularly prone to such false calls. This sample-specific length bias is effectively removed by the conditional quantile normalization (cqn) and EDASeq methods, which allow the integration of gene length as a sample-specific covariate. Consequently, using these normalization methods led to substantial reduction in GSEA false results while retaining true ones. In addition, we found that application of gene-set tests that take into account gene-gene correlations attenuates false positive rates caused by the length bias, but statistical power is reduced as well. Our results advocate the inspection and correction of sample-specific length biases as default steps in RNA-seq analysis pipelines and reiterate the need to account for intergene correlations when performing gene-set enrichment tests to lessen false interpretation of transcriptomic data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Bias
  • Databases, Genetic
  • Datasets as Topic
  • Humans
  • Mice
  • RNA / chemistry*
  • Sequence Analysis, RNA / standards*
  • Transcriptome

Substances

  • RNA

Grant support

This study was supported by the DIP German-Israeli project cooperation, the Israel Science Foundation grant no. 2118/19, and the Koret-UC Berkeley-Tel Aviv University Initiative in Computational Biology and Bioinformatics to RE and by the VWM Saxby project grant to OE-S. RE is a Faculty Fellow of the Edmond J. Safra Center for Bioinformatics at Tel Aviv University. SM fellowship is supported by Sagol School of Neuroscience. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.