title: A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data.
abstract: Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred.
authors.affiliation: Systems Analytics Inc., Waltham, MA, USA.
authors.forename: J
authors.initials: J
authors.lastname: Luo
authors.forename: M
authors.initials: M
authors.lastname: Schumacher
authors.forename: A
authors.initials: A
authors.lastname: Scherer
authors.forename: D
authors.initials: D
authors.lastname: Sanoudou
authors.forename: D
authors.initials: D
authors.lastname: Megherbi
authors.forename: T
authors.initials: T
authors.lastname: Davison
authors.forename: T
authors.initials: T
authors.lastname: Shi
authors.forename: W
authors.initials: W
authors.lastname: Tong
authors.forename: L
authors.initials: L
authors.lastname: Shi
authors.forename: H
authors.initials: H
authors.lastname: Hong
authors.forename: C
authors.initials: C
authors.lastname: Zhao
authors.forename: F
authors.initials: F
authors.lastname: Elloumi
authors.forename: W
authors.initials: W
authors.lastname: Shi
authors.forename: R
authors.initials: R
authors.lastname: Thomas
authors.forename: S
authors.initials: S
authors.lastname: Lin
authors.forename: G
authors.initials: G
authors.lastname: Tillinghast
authors.forename: G
authors.initials: G
authors.lastname: Liu
authors.forename: Y
authors.initials: Y
authors.lastname: Zhou
authors.forename: D
authors.initials: D
authors.lastname: Herman
authors.forename: Y
authors.initials: Y
authors.lastname: Li
authors.forename: Y
authors.initials: Y
authors.lastname: Deng
authors.forename: H
authors.initials: H
authors.lastname: Fang
authors.forename: P
authors.initials: P
authors.lastname: Bushel
authors.forename: M
authors.initials: M
authors.lastname: Woods
authors.forename: J
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods / Q000592:standards; D006801:Humans; D008113:Liver Neoplasms / Q000188:drug therapy / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D009841:Oligonucleotides; D011237:Predictive Value of Tests; D011786:Quality Control; D012015:Reference Standards; D015203:Reproducibility of Results; D043922:Toxicogenetics / Q000706:statistics & numerical data
chemical_list: D009841:Oligonucleotides