title: Covariance adjustment for batch effect in gene expression data.
abstract: Batch bias has been found in many microarray gene expression studies that involve multiple batches of samples. A serious batch effect can alter not only the distribution of individual genes but also the inter-gene relationships. Even though some efforts have been made to remove such bias, there has been relatively less development on a multivariate approach, mainly because of the analytical difficulty due to the high-dimensional nature of gene expression data. We propose a multivariate batch adjustment method that effectively eliminates inter-gene batch effects. The proposed method utilizes high-dimensional sparse covariance estimation based on a factor model and a hard thresholding. Another important aspect of the proposed method is that if it is known that one of the batches is produced in a superior condition, the other batches can be adjusted so that they resemble the target batch. We study high-dimensional asymptotic properties of the proposed estimator and compare the performance of the proposed method with some popular existing methods with simulated data and gene expression data sets.
authors.affiliation: Division of Public Health Sciences, Washington University in St. Louis, St. Louis, MO 63110, U.S.A.
authors.forename: Jung Ae
authors.initials: JA
authors.lastname: Lee
authors.forename: Kevin K
authors.initials: KK
authors.lastname: Dobbin
authors.forename: Jeongyoun
authors.initials: J
authors.lastname: Ahn
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D046228:Microarray Analysis / Q000379:methods*
keywords: batch effect; factor model; gene expression; high-dimensional covariance estimation