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Article

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

Bioinformatics (Oxford, England) - 15 Nov 2013

Reese Sarah E, Archer Kellie J, Therneau Terry M, Atkinson Elizabeth J, Vachon Celine M, de Andrade Mariza, Kocher Jean-Pierre A, Eckel-Passow Jeanette E

Abstract excerpt

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...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 23958724
DOI: 10.1093/bioinformatics/btt480
PMCID: PMC3810845

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