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

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 leadi

SUBMITTER: Mandelboum S 

PROVIDER: S-EPMC6850523 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature