ArticleBMC bioinformatics2016
Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson's disease.Nature communications · 2025Pooled it
- Estropausal gut microbiota transplant improves measures of ovarian function in adult mice.Nature aging · 2026Article
- Development and comparison of evaluation metrics for batch correction reveals performance differences.Bioinformatics advances · 2026Article
- Techniques for learning and transferring knowledge for microbiome-based classification and prediction: review and assessment.Briefings in bioinformatics · 2024Review
- Thinking points for effective batch correction on biomedical data.Briefings in bioinformatics · 2024Article
- A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.BMC bioinformatics · 2024Article
- Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques.Scientific reports · 2023Article
- Development of anMolecular therapy. Methods & clinical development · 2023Article
- Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information.Journal of agricultural and food chemistry · 2022Article
- Network meta-analysis of transcriptome expression changes in different manifestations of dengue virus infection.BMC genomics · 2022Article
- A Survey of Statistical Methods for Microbiome Data Analysis.Frontiers in applied mathematics and statistics · 2022Article
- Predicting genotoxicity of viral vectors for stem cell gene therapy using gene expression-based machine learning.Molecular therapy : the journal of the American Society of Gene Therapy · 2021Article
- Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study.Radiation oncology (London, England) · 2020Article
- Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics.iScience · 2020Article
- Network meta-analysis correlates with analysis of merged independent transcriptome expression data.BMC bioinformatics · 2019Article
- Molecular subtypes of triple-negative breast cancer in women of different race and ethnicity.Oncotarget · 2019Article
- Batch adjustment by reference alignment (BARA): Improved prediction performance in biological test sets with batch effects.PloS one · 2019Article
- Towards evidence-based computational statistics: lessons from clinical research on the role and design of real-data benchmark studies.BMC medical research methodology · 2017Article
- A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies.Scientific reports · 2017Article
- MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms.BMC bioinformatics · 2017Article
Corrections and comments
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the context of high-throughput molecular data analysis it is common that the observations included in a dataset form distinct groups; for example, measured at different times, under different conditions or even in different labs. These groups are generally denoted as batches. Systematic differences between these batches not attributable to the biological signal of interest are denoted as batch effects. If ignored when conducting analyses on the combined data, batch effects can lead to distortions in the results. In this paper we present FAbatch, a general, model-based method for correcting for such batch effects in the case of an analysis involving a binary target variable. It is a combination of two commonly used approaches: location-and-scale adjustment and data cleaning by adjustment for distortions due to latent factors. We compare FAbatch extensively to the most commonly applied competitors on the basis of several performance metrics. FAbatch can also be used in the context of prediction modelling to eliminate batch effects from new test data. This important application is illustrated using real and simulated data. We implemented FAbatch and various other functionalities in the R package bapred available online from CRAN.
resultsFAbatch is seen to be competitive in many cases and above average in others. In our analyses, the only cases where it failed to adequately preserve the biological signal were when there were extremely outlying batches and when the batch effects were very weak compared to the biological signal.
conclusionsAs seen in this paper batch effect structures found in real datasets are diverse. Current batch effect adjustment methods are often either too simplistic or make restrictive assumptions, which can be violated in real datasets. Due to the generality of its underlying model and its ability to perform well FAbatch represents a reliable tool for batch effect adjustment for most situations found in practice.
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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.