ArticleThe pharmacogenomics journal2010
A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data.
Article in The pharmacogenomics journal, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 151 papers, 2 of them syntheses that pooled it.
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Who cites it
151 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- P-value evaluation, variability index and biomarker categorization for adaptively weighted Fisher's meta-analysis method in omics applications.Bioinformatics (Oxford, England) · 2020Pooled it
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- Multi-platform integration of brain and CSF proteomes reveals biomarker panels for Alzheimer's disease.Briefings in bioinformatics · 2026Article
- Development and comparison of evaluation metrics for batch correction reveals performance differences.Bioinformatics advances · 2026Article
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch.Frontiers in microbiology · 2026Article
- A clustering-stratified cross-validation framework for validating omics survival models: application to head and neck cancer.BMC medical research methodology · 2025Article
- Protein-level batch-effect correction enhances robustness in MS-based proteomics.Nature communications · 2025Article
- High performance data integration for large-scale analyses of incomplete Omic profiles using Batch-Effect Reduction Trees (BERT).Nature communications · 2025Article
- Interpretable Machine Learning for Cross-Cohort Prediction of Motor Fluctuations in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.NPJ digital medicine · 2025Article
- Plasma Proteomic Profiling of a Group of Anxious Dogs by LC-MS/MS: A Case-Control Study.Proteomics. Clinical applications · 2025Article
- Deciphering the Oncogenic Landscape of Hepatocytes Through Integrated Single-Nucleus and Bulk RNA-Seq of Hepatocellular Carcinoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Assessing and mitigating batch effects in large-scale omics studies.Genome biology · 2024Review
- Reliable biological and multi-omics research through biometrology.Analytical and bioanalytical chemistry · 2024Review
- Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.Nature biotechnology · 2024Article
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.Environmental science & technology · 2024Article
91 more citing papers are in PubMed but not listed here.
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Authors and funding
25 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.