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      Variation-preserving normalization unveils blind spots in gene expression profiling

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      bioRxiv

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          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 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.

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          Author and article information

          Journal
          bioRxiv
          June 19 2015
          Article
          10.1101/021212
          a63c590e-d687-4a9d-9af8-461b1da44409
          © 2015
          Product
          Self URI (article page): http://biorxiv.org/lookup/doi/10.1101/021212

          Human biology,Genetics
          Human biology, Genetics

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