Jun 19, 2015

Variation-preserving normalization unveils blind spots in gene expression profiling

BioRxiv : the Preprint Server for Biology
Carlos P RocaJaneck J Scott-Fordsmand

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 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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Mentioned in this Paper

Study
Microarray Analysis
Cellular Process
Genes
Profile (Lab Procedure)
Gene Expression
Gene Expression Profiling
Analysis
Blinded
RNA

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