ArticleGenome research2025
Batch correction methods used in single-cell RNA sequencing analyses are often poorly calibrated.
Article in Genome research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- Spatiotemporal transcriptomics for tumour microenvironment remodelling in cancer progression and therapy.Clinical and translational medicine · 2026Review
- Transcriptomic responses to endurance exercise training in rats.BMC genomic data · 2026Article
- A multi-center study on the consistency of drug sensitivity testing in patients with acute myeloid leukemia.NPJ precision oncology · 2026Article
- Black-box data: a new paradigm for biomedicine in the AI era.Chemical science · 2026Review
- Integration of large, complex single-cell datasets with Harmony2.bioRxiv : the preprint server for biology · 2026Article
- Pitfalls in analysis and interpretation of single-cell RNA-seq data in cancer.Neuro-oncology advances · 2026Review
- DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data.bioRxiv : the preprint server for biology · 2026Article
- Quantifying pathological progression from single-cell transcriptomic data with scPSS.Genome research · 2026Article
- OTMODE: an optimal transport theory-based framework for identifying differential features in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- Deciphering immune-inflammatory dysregulation in the endometriotic microenvironment: insights from single-cell omics and artificial intelligence.Frontiers in immunology · 2026Review
- Investigating tryptophan metabolism in colorectal cancer using Single-cell RNA sequencing based on machine learning techniques.PloS one · 2026Article
- Development and comparison of evaluation metrics for batch correction reveals performance differences.Bioinformatics advances · 2026Article
- CycleMix: Gaussian mixture modeling of the cell cycle.Bioinformatics advances · 2026Article
- ScGeo reveals non-canonical trajectories beyond RNA velocity in radiation-induced hematopoietic recovery.Bioinformatics advances · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- A unified genetic perturbation language for human cellular programming.bioRxiv : the preprint server for biology · 2025Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Massively parallel in vivo Perturb-seq screening.Nature protocols · 2025Review
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer.Nature communications · 2024Article
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Abstract
As the number of experiments that employ single-cell RNA sequencing (scRNA-seq) grows, it opens up the possibility of combining results across experiments or processing cells from the same experiment assayed in separate sequencing runs. The gain in the number of cells that can be compared comes at the cost of batch effects that may be present. Several methods have been proposed to combat this for scRNA-seq data sets. We compare eight widely used methods used for batch correction of scRNA-seq data sets. We present a novel approach to measure the degree to which the methods alter the data in the process of batch correction, both at the fine scale, comparing distances between cells, as well as measuring effects observed across clusters of cells. We demonstrate that many of the published methods are poorly calibrated in the sense that the process of correction creates measurable artifacts in the data. In particular, MNN, SCVI, and LIGER perform poorly in our tests, often altering the data considerably. Batch correction with Combat, ComBat-seq, BBKNN, and Seurat introduces artifacts that could be detected in our setup. However, we find that Harmony is the only method that consistently performs well in all the testing methodology we present. Therefore, Harmony is the only method we recommend using when performing batch correction of scRNA-seq data.
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