Removing technical variability in RNA-seq data using conditional quantile normalization.

ABSTRACT: The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left unco

SUBMITTER: Hansen KD 

PROVIDER: S-EPMC3297825 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature