Practical impacts of genomic data "cleaning" on biological discovery using surrogate variable analysis.

ABSTRACT:

Background

Genomic data production is at its highest level and continues to increase, making available novel primary data and existing public data to researchers for exploration. Here we explore the consequences of "batch" correction for biological discovery in two publicly available expression datasets. We consider this to include the estimation of and adjustment for wide-spread systematic heterogeneity in genomic measurements that is unrelated to the effects under study, whether it be technical or biological in nature.

Methods

We present three illustrative data analyses using surrogate variable analysis (SVA) and describe how to perform artifact discovery in light of natural heterogeneity within biological groups, secondary biological questions of interest, and non-linear

SUBMITTER: Jaffe AE 

PROVIDER: S-EPMC4636836 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature