Variation-preserving normalization unveils blind spots in gene expression profiling

TL;DR

This paper introduces a new normalization method for gene expression data that does not assume most genes are unchanged, revealing greater variation and improving reproducibility in transcriptomics studies.

Contribution

A novel mathematical normalization approach that does not rely on the assumption that most genes are not differentially expressed, addressing reproducibility issues.

Findings

Gene expression variation is larger than previously believed.

The new normalization improves reproducibility of transcriptomics data.

It enhances detection of complex gene expression modulations.

Abstract

RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much larger than currently believed, and that it can be measured with available assays. Our results also explain, at least partially, the reproducibility problems encountered in transcriptomics studies. We expect that this improvement in detection will help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression.