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Reese SE, Archer KJ, Therneau TM, et al. A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics. 2013;29(22):2877-83doi: 10.1093/bioinformatics/btt480.
Reese, S. E., Archer, K. J., Therneau, T. M., Atkinson, E. J., Vachon, C. M., de Andrade, M., Kocher, J. P., & Eckel-Passow, J. E. (2013). A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics (Oxford, England), 29(22), 2877-83. https://doi.org/10.1093/bioinformatics/btt480
Reese, Sarah E, et al. "A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis." Bioinformatics (Oxford, England) vol. 29,22 (2013): 2877-83. doi: https://doi.org/10.1093/bioinformatics/btt480
Reese SE, Archer KJ, Therneau TM, Atkinson EJ, Vachon CM, de Andrade M, Kocher JP, Eckel-Passow JE. A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics. 2013 Nov 15;29(22):2877-83. doi: 10.1093/bioinformatics/btt480. Epub 2013 Aug 19. PMID: 23958724; PMCID: PMC3810845.
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Bioinformatics. 2013 Nov 15;29(22):2877-83. doi: 10.1093/bioinformatics/btt480. Epub 2013 Aug 19.

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

Bioinformatics (Oxford, England)

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

Affiliations

  1. Department of Biostatistics, Biostatistics Shared Resource Core, VCU Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA, Division of Biomedical Statistics and Informatics and Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.

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

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 gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays, whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies.

CONCLUSION: We developed a new statistic that uses gPCA to identify whether batch effects exist in high-throughput genomic data. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well.

AVAILABILITY AND IMPLEMENTATION: The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article.

CONTACT: [email protected]

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