title: Variation-preserving normalization unveils blind spots in gene expression profiling.
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.
authors.affiliation: Department of Chemical Engineering, Universitat Rovira i Virgili, 43007 Tarragona, Spain.|Department of Bioscience, Aarhus University, 8600 Silkeborg, Denmark.
authors.forename: Carlos P
authors.initials: CP
authors.lastname: Roca
authors.affiliation: Department of Biology &CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
authors.forename: Susana I L
authors.initials: SI
authors.lastname: Gomes
authors.affiliation: Department of Biology &CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
authors.forename: Mónica J B
authors.initials: MJ
authors.lastname: Amorim
authors.affiliation: Department of Bioscience, Aarhus University, 8600 Silkeborg, Denmark.
authors.forename: Janeck J
authors.initials: JJ
authors.lastname: Scott-Fordsmand