Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

ABSTRACT:

Background

With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies.

Focus

The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is

SUBMITTER: Soneson C 

PROVIDER: S-EPMC4072626 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature