Observational studyPloS one2016
Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data.
Observational study in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- DNA methylation-based biomarkers of aging were slowed down in a two-year diet and physical activity intervention trial: the DAMA study.Aging cell · 2021Trial
- Blood biomarkers predict conversion from cognitively stable to mild cognitive impairment or Alzheimer's disease in Down syndrome at 16-month follow-up in ABC-DS.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Achieving Effective Batch-to-Batch Error Correction through Suppression Correction and Dual MSTUS Normalization.Analytical chemistry · 2026Article
- Defining PI3K Signaling as a Key Regulator of Molecular Heterogeneity and Inflammation in Glomerular Lupus Nephritis.Immune network · 2026Article
- Integrative transcriptomic and structural modeling reveal CASP1, TLR3, PYCARD, and CD274 as immune-modulatory drivers in breast cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- A Robust Serum Proteomic Signature of the E2 Allele of Apolipoprotein E.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Integrated machine learning and causal inference reveal CHRDL1 as a diagnostic and functional biomarker in NSCLC.Discover oncology · 2025Article
- Identification of Mitochondrial and Succinylation Modification-Related Gene Signature in Ischemic Stroke.Molecular neurobiology · 2025Article
- How far are we from the era of big data in transcriptomics? Lessons from the bacterial data in GEO.Briefings in bioinformatics · 2025Review
- Targeting neutrophil-driven inflammation in adult-onset still's disease: molecular insights from gene expression profiles.Arthritis research & therapy · 2025Article
- Systemic Inflammation and Metabolic Changes After Cardiac Surgery and Postoperative Delirium Risk.Journal of clinical medicine · 2025Article
- Human skeletal muscle fiber heterogeneity beyond myosin heavy chains.Nature communications · 2025Article
- HarmonizR: blocking and singular feature data adjustment improve runtime efficiency and data preservation.BMC bioinformatics · 2025Article
- Hepatic zinc deficiency dampens the acute phase response in patients with alcohol-associated hepatitis.Frontiers in immunology · 2025Article
- Machine learning-based identification of core regulatory genes in hepatocellular carcinoma: insights from lactylation modification and liver regeneration-related genes.Frontiers in oncology · 2025Article
- Identification of Mitophagy-Related Genes and Analysis of Immune Infiltration in Atherosclerosis.Journal of inflammation research · 2025Article
- iComBat: An incremental framework for batch effect correction in DNA methylation array data.Computational and structural biotechnology journal · 2025Article
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
26 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.
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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.