Batch adjustment by reference alignment (BARA): Improved prediction performance in biological test sets with batch effects.

ABSTRACT: Many biological data acquisition platforms suffer from inadvertent inclusion of biologically irrelevant variance in analyzed data, collectively termed batch effects. Batch effects can lead to difficulties in downstream analysis by lowering the power to detect biologically interesting differences and can in certain instances lead to false discoveries. They are especially troublesome in predictive modelling where samples in training sets and test sets are often completely correlated with batches. In this article, we present BARA, a normalization method for adjusting batch effects in predictive modelling. BARA utilizes a few reference samples to adjust for batch effects in a compressed data space spanned by the training set. We evaluate BARA using a collection of publicly available datasets a

SUBMITTER: Gradin R 

PROVIDER: S-EPMC6386283 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature