title: A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis.
abstract: BACKGROUND
  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.
  
  
  CONCLUSIONS
  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.
  
  
  METHODS
  The gPCA R package (Available via CRAN) provides functionality and data to perform the methods in this article.
  
  
  BACKGROUND
  reesese@vcu.edu
authors: 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.
authors: Sarah E
authors: SE
authors: Reese
authors: Kellie J
authors: KJ
authors: Archer
authors: Terry M
authors: TM
authors: Therneau
authors: Elizabeth J
authors: EJ
authors: Atkinson
authors: Celine M
authors: CM
authors: Vachon
authors: Mariza
authors: M
authors: de Andrade
authors: Jean-Pierre A
authors: JP
authors: Kocher
authors: Jeanette E
authors: JE
authors: Eckel-Passow
mesh_terms: D056915:DNA Copy Number Variations; D003627:Data Interpretation, Statistical; D023281:Genomics / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis
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