ArticleBMC bioinformatics2019
Batch correction evaluation framework using a-priori gene-gene associations: applied to the GTEx dataset.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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24 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- A kernel density estimation-based approach for quantifying O-GlcNAcylation dysregulation in cancer from gene expression data.Bioinformatics advances · 2026Article
- Tissue-specific co-expression patterns of BAF complex genes across human endocrine and non-endocrine tissues.Endocrine connections · 2025Article
- A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions.Nature aging · 2025Article
- CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.Nucleic acids research · 2025Article
- A computational framework for detecting inter-tissue gene-expression coordination changes with aging.Scientific reports · 2025Article
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.Nature biotechnology · 2024Article
- Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.Laboratory investigation; a journal of technical methods and pathology · 2024Article
- CoGTEx: Unscaled system-level coexpression estimation from GTEx data forecast novel functional gene partners.PloS one · 2024Article
- Transcription Factors and ncRNAs Associated withBiomedicines · 2022Article
- Network analysis reveals dysregulated functional patterns in type II diabetic skin.Scientific reports · 2022Article
- Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries.BMC genomics · 2022Article
- Comparison of confound adjustment methods in the construction of gene co-expression networks.Genome biology · 2022Article
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- Tissue, age, sex, and disease patterns of matrisome expression in GTEx transcriptome data.Scientific reports · 2021Article
- GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package.BMC bioinformatics · 2021Article
- Exploring the Impact of Cerebrovascular Disease and Major Depression on Non-diseased Human Tissue Transcriptomes.Frontiers in genetics · 2021Article
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3 authors.
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Abstract
backgroundCorrecting a heterogeneous dataset that presents artefacts from several confounders is often an essential bioinformatics task. Attempting to remove these batch effects will result in some biologically meaningful signals being lost. Thus, a central challenge is assessing if the removal of unwanted technical variation harms the biological signal that is of interest to the researcher.
resultsWe describe a novel framework, B-CeF, to evaluate the effectiveness of batch correction methods and their tendency toward over or under correction. The approach is based on comparing co-expression of adjusted gene-gene pairs to a-priori knowledge of highly confident gene-gene associations based on thousands of unrelated experiments derived from an external reference. Our framework includes three steps: (1) data adjustment with the desired methods (2) calculating gene-gene co-expression measurements for adjusted datasets (3) evaluating the performance of the co-expression measurements against a gold standard. Using the framework, we evaluated five batch correction methods applied to RNA-seq data of six representative tissue datasets derived from the GTEx project.
conclusionsOur framework enables the evaluation of batch correction methods to better preserve the original biological signal. We show that using a multiple linear regression model to correct for known confounders outperforms factor analysis-based methods that estimate hidden confounders. The code is publicly available as an R package.
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