  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.
