Adjusting batch effects in microarray expression data using empirical Bayes methods.

By W. Johnson, Cheng Li, Ariel Rabinovic. Biostatistics, 2007

8,026 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/427eaafcd47b3abc620b953ff021f25925ff0e62

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In Molecular Biology

Citations in the corpus

Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease (2018, methodology): “Following quantile normalization, batch correction was performed using ‘ComBat’ algorithm implemented in R package ‘sva’ (Johnson et al., 2007).”
Incomplete DNA methylation underlies a transcriptional memory of the somatic cell in human iPS cells (2011, positive): “The data were normalized together using RMA and then corrected for potential batch effects using an empirical Bayes metho”
Harmonization of cortical thickness measurements across scanners and sites (2017, methodology): “The Combat harmonization model (Johnson et al., 2007) extends the adjusted residuals harmonization model presented in Equation (2) in two ways: (1) it models site-speci ﬁ c scaling factors and (2) it uses empirical Bayes to improve the estimation of the site parameters for small sample sizes.”
Epigenetic Signatures of Cigarette Smoking (2016, result): “We also confirmed previously reported findings for CpGs annotated to AHRR, RARA, and LRRN3.”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, methodology): “We compare our method to the more standard pipeline of batch correction ComBat [40] followed by principal component analysis (Methods 4.”
Epigenetic Predictor of Age (2011, methodology): “Batch effect were removed using the Combat algorithm [9], and one outlier sample was removed.”
Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value (2013, methodology): “For the CIT dataset, residual technical batch effects were corrected using the ComBat method implemented in the SVA R package [20].”
Specification of tissue-resident macrophages during organogenesis (2016, methodology): “For visualization and illustration purposes (PCA, supplementary tables, heatmaps), we used values adjusted by variance stabilizing transformation from DESeq2 in which batch-effects had been corrected for with ComBat (47).”
COMPREHENSIVE CLASSIFICATION OF RETINAL BIPOLAR NEURONS BY SINGLE-CELL TRANSCRIPTOMICS (2016, positive): “…2) Jun 1e-6 −0.82 Kif1bp 1e-6 −0.80 Hes1 1e-6 −0.78 Gm29609 1e-6 0.76 NCBP2-AS2 1e-6 −0.74 Gm4792 1e-6 0.72 Prss22 1e-6 0.70 Sag 1e-6 −0.70 We performed batch correction on the expression matrix Eij using the ComBat method (Johnson et al., 2007) as implemented in the R package sva.”
Batch effect removal methods for microarray gene expression data integration: a survey (2013, methodology): “Empirical Bayes (EB) [14] (also known as Extended Johnson-Li-Rabinovich or COMBAT) is a method using estimations for the LS parameters (mean and variance) for each gene.”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “An R tool, ComBat, with tumor/normal status adjustment was applied to remove batch effects (Johnson et al., 2007).”
Functional normalization of 450k methylation array data improves replication in large cancer studies (2014, neutral): “In contrast, supervised normalization methods, such as SVA [25,26], ComBat [27], SNM [24] and RUV [28], require the user to provide either batch parameters or an outcome of interest.”