Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset.

ABSTRACT:

Background

Correcting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher.

Results

We describe a novel framework, B-CeF, to evaluate the effectiveness of batch correction methods and their tendency toward over or under correction. The approach is based on comparing co-expression of adjusted gene-gene pairs to a-priori knowledge of highly confident gene-gene associations based on thousands of unrelated experiments derived from an external reference. Our framewo

SUBMITTER: Somekh J 

PROVIDER: S-EPMC6537327 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature