ArticleScientific reports2017
A Novel Statistical Method to Diagnose, Quantify and Correct Batch Effects in Genomic Studies.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch.Frontiers in microbiology · 2026Article
- Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.Scientific reports · 2025Article
- Article
- Gut microbiome and plasma metabolome alterations in ileostomy and after closure of ileostomy.Microbiology spectrum · 2025Article
- Article
- BatchFLEX: feature-level equalization of X-batch.Bioinformatics (Oxford, England) · 2024Article
- Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research.Cancers · 2024Article
- Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs.BMC bioinformatics · 2024Article
- Pyroptosis is involved in the immune microenvironment regulation of unexplained recurrent miscarriage.Mammalian genome : official journal of the International Mammalian Genome Society · 2024Article
- Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning.Science advances · 2024Article
- Principled and interpretable alignability testing and integration of single-cell data.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Exploring common pathogenic association between Epstein Barr virus infection and long-COVID by integrating RNA-Seq and molecular dynamics simulations.Frontiers in immunology · 2024Article
- APPLICATION OF QUANTILE DISCRETIZATION AND BAYESIAN NETWORK ANALYSIS TO PUBLICLY AVAILABLE CYSTIC FIBROSIS DATA SETS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- A primer on correlation-based dimension reduction methods for multi-omics analysis.Journal of the Royal Society, Interface · 2023Review
- On the variability and dependence of human leg stiffness across strides during running and some consequences for the analysis of locomotion data.Royal Society open science · 2023Article
- Considerations for reproducible omics in aging research.Nature aging · 2023Review
- Batch correction methods for nontarget chemical analysis data: application to a municipal wastewater collection system.Analytical and bioanalytical chemistry · 2023Article
- Comprehensive characterization of immune landscape of Indian and Western triple negative breast cancers.Translational oncology · 2022Article
- Local data commons: the sleeping beauty in the community of data commons.BMC bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome projects now generate large-scale data often produced at various time points by different laboratories using multiple platforms. This increases the potential for batch effects. Currently there are several batch evaluation methods like principal component analysis (PCA; mostly based on visual inspection), and sometimes they fail to reveal all of the underlying batch effects. These methods can also lead to the risk of unintentionally correcting biologically interesting factors attributed to batch effects. Here we propose a novel statistical method, finding batch effect (findBATCH), to evaluate batch effect based on probabilistic principal component and covariates analysis (PPCCA). The same framework also provides a new approach to batch correction, correcting batch effect (correctBATCH), which we have shown to be a better approach to traditional PCA-based correction. We demonstrate the utility of these methods using two different examples (breast and colorectal cancers) by merging gene expression data from different studies after diagnosing and correcting for batch effects and retaining the biological effects. These methods, along with conventional visual inspection-based PCA, are available as a part of an R package exploring batch effect (exploBATCH; https://github.com/syspremed/exploBATCH ).
Indexed as
Identifiers
What OpenQuestion holds
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.