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.

Potential Impact of Microarray Diagnosis of T Cell–Mediated Rejection in Kidney Transplants: The INTERCOM Study

“…BFC403 was normalized as a separate batch as described previously (14), also using RefPlus. Because Affymetrix changed their labeling kit after the BFC403 biopsies were processed, the BFC403 expression values were adjusted for this batch effect using the Ratio-G method (27) after normalization. All analyses and graphics were done using the ''R'' software package, version 2.12.1 (64-bit), with various libraries from Bioconductor 2.8.…”

Explanation of Variability and Removal of Confounding Factors from Data through Optimal Transport

“…A number of techniques have been developed for removing batch effects; see, for instance, the two reviews in [7,16]. Here we propose a novel approach based on the theory of optimal transport.…”

“…Classical methodologies include ANOVA, ANCOVA, and MANCOVA [20], the many variations of factor analysis [18] (all of these involve linear regression combined with statistical models), stratification [8], detrending of time series, various methodologies for the removal of batch effects [7,16], and a large number of specialized techniques developed within individual fields-notoriously biostatistics, psychology, sociology, econometrics, and the environmental sciences. An alternative methodology involves the statistical elimination of confounding effects by the design of randomized experiments [17], as opposed to the observational studies where covariates are unavoidable.…”

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

“…caused by experimental artefacts [39][40][41][42], which can be present when experimental data are collected. If replicates of the same scenario are available (i.e.…”

“…several experimental runs with the same strain and same uptake rates for each substrate), the presence of batch effects could be removed. Otherwise, the bias introduced by the non-biological nature of this kind of effects may confound true biological differences [41], affecting the results of statistical analysis. In this study, the scenarios have no replicates (see Figure 2).…”