A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data
By J. Luo, Martin M. Schumacher, A. Scherer, D. Sanoudou, Dalila B. Megherbi, T. Davison, Tieliu Shi, W. Tong, Leming Shi, H. Hong and 15 more. The Pharmacogenomics Journal, 2010
319 citations recorded by the source corpus.
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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/88fdf23c3a72c99a02100d147bc889ab55e291d6
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In Molecular Biology
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- Charlotte Soneson
- Mauro Delorenzi
- S. Gerster
- Mark D. Robinson
- Alicia Oshlack
- Gordon K. Smyth
- Susan P. Holmes
Citations in the corpus
- Batch effect removal methods for microarray gene expression data integration: a survey (2013, methodology): “Nevertheless, the reader should also be aware that combining datasets using inefficient methods could also result in misleading findings [12, 26].”
- Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation (2014, background): “Batch effects can be a big obstacle when combining data sets, and their characterization and potential elimination have recently received much attention in the literature (e.g., [7,8,23,25]).”