Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

By Chao Chen, Kay S. Grennan, J. Badner, Dandan Zhang, E. Gershon, Li Jin, Chunyu Liu. PLoS ONE, 2011

519 citations recorded by the source corpus.

The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cf4ac8c588c71fd30892a6b5125dfc13a263dfb9

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Citations in the corpus

Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells (2016, background): “4.14 N/A http://bedops.readthedocs.io/en/latest/ ComBat Chen et al., 2011 http://www.bioconductor.org/packages/release/bioc/html/ sva.html PEER Stegle et al., 2012 https://github.com/PMBio/peer/wiki DNA Methylation Age Calculator Horvath, 2013 https://dnamage.genetics.ucla.edu/ LIMIX Casale et al.,…”
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]).”
Batch effect removal methods for microarray gene expression data integration: a survey (2013, background): “As a consequence, batch effects are ‘almost inevitable’, but there are only few researchers who address this problem in their analysis flow (see [16] for some concluding figures on the number of articles published from 1 January 2010 to 1 July 2010, which addressed this problem).”
Controlling Attribute Effect in Linear Regression (2013, background): “Regarding these collections, however, Chen et al. [20] state that " data produced by the thousands of micro-array studies published annually are confounded by " batch effects, " the systematic error introduced when samples are processed in multiple batches.”
Explanation of Variability and Removal of Confounding Factors from Data through Optimal Transport (2018, methodology): “A number of techniques have been developed for removing batch effects; see, for instance, the two reviews in [7, 16].”