ArticlePloS one2018
Comparison of statistical methods and the use of quality control samples for batch effect correction in human transcriptome data.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
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- Ovarian hormone deficiency enhances wood smoke-induced immune dysfunction via transcriptomic and metabolic alterations.bioRxiv : the preprint server for biology · 2026Article
- A teaching proposal for a short course on biomedical data science.PLoS computational biology · 2025Article
- Effects of particulate air pollution exposure on lung-brain axis and related miRNAs modulation in mouse models.Frontiers in cell and developmental biology · 2025Review
- Empirical Characterization of False Discovery Rates of Differentially Expressed Genes and Transcriptomic Benchmark Concentrations in Zebrafish Embryos.Environmental science & technology · 2024Article
- Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA.Nature protocols · 2023Review
- Prenatal Exposure to Metabolism-Disrupting Chemicals, Cord Blood Transcriptome Perturbations, and Birth Weight in a Belgian Birth Cohort.International journal of molecular sciences · 2023Article
- Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham.PloS one · 2023Article
- A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer.Journal of clinical medicine · 2022Article
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
- Article
- Microarray Data Preprocessing: From Experimental Design to Differential Analysis.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Using genome-wide expression compendia to study microorganisms.Computational and structural biotechnology journal · 2022Review
- Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies.Bioinformatics (Oxford, England) · 2021Article
- Guidelines for biomarker discovery in endometrium: correcting for menstrual cycle bias reveals new genes associated with uterine disorders.Molecular human reproduction · 2021Article
- Identification of the Molecular Subgroups in Idiopathic Pulmonary Fibrosis by Gene Expression Profiles.Computational and mathematical methods in medicine · 2021Article
- Coming up short: Comparing venous blood, dried blood spots & saliva samples for measuring telomere length in health equity research.PloS one · 2021Article
- Correcting for experiment-specific variability in expression compendia can remove underlying signals.GigaScience · 2020Article
- Molecular and biochemical responses of vitellogenin in the mussel Mytilus galloprovincialis exposed to the glyphosate-based herbicide Roundup® Power 2.0.Environmental science and pollution research international · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding "treatment" and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design.
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Registered trials
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