RT Journal Article SR Electronic T1 Variation-preserving normalization unveils blind spots in gene expression profiling JF bioRxiv FD Cold Spring Harbor Laboratory SP 021212 DO 10.1101/021212 A1 Carlos P. Roca A1 Susana I. L. Gomes A1 Mónica J. B. Amorim A1 Janeck J. Scott-Fordsmand YR 2015 UL http://biorxiv.org/content/early/2015/12/04/021212.abstract AB 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 an 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 greater than currently believed, and that it can be measured with available technologies. Our results also explain, at least partially, the problems encountered in transcriptomics studies. We expect this improvement in detection to help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression.