A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.

ABSTRACT:

Motivation

Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal component analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data.

Results

We present an extension of PCA to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test whether a batch effect exists. We apply our proposed test statistic derived using

SUBMITTER: Reese SE 

PROVIDER: S-EPMC3810845 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature