ArticleBioinformatics (Oxford, England)2020
scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression.BMC bioinformatics · 2022Pooled it
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- RAGCell: retrieval-augmented generation as supervision for versatile single-cell analysis.Bioinformatics (Oxford, England) · 2026Article
- COBRA: cell-type-specific orthogonal batch effect removal algorithm in single cell RNA-sequencing data.Bioinformatics (Oxford, England) · 2026Article
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Towards the tumor microenvironment scoring methods for immune checkpoint inhibitor response.NPJ precision oncology · 2026Article
- The Clinical Prognostic Value of Lactylation-Regulated Proteins in Gastric Cancer.Journal of proteome research · 2026Article
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- The effect of data transformation on low-dimensional integration of single-cell RNA-seq.BMC bioinformatics · 2024Article
- Review
- Meta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome.NPJ microgravity · 2023Article
- SelectBCM tool: a batch evaluation framework to select the most appropriate batch-correction methods for bulk transcriptome analysis.NAR genomics and bioinformatics · 2023Article
- Differential gene expression of immunity and inflammation genes in colorectal cancer using targeted RNA sequencing.Frontiers in oncology · 2023Article
- The Sum of Two Halves May Be Different from the Whole-Effects of Splitting Sequencing Samples Across Lanes.Genes · 2022Article
- Effective and scalable single-cell data alignment with non-linear canonical correlation analysis.Nucleic acids research · 2022Article
- WNT5A drives interleukin-6-dependent epithelial-mesenchymal transition via the JAK/STAT pathway in keloid pathogenesis.Burns & trauma · 2022Article
- Batch effect reduction of microarray data with dependent samples using an empirical Bayes approach (BRIDGE).Statistical applications in genetics and molecular biology · 2021Article
- Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States inSensors (Basel, Switzerland) · 2021Article
- CSS: cluster similarity spectrum integration of single-cell genomics data.Genome biology · 2020Article
- Accounting for cell type hierarchy in evaluating single cell RNA-seq clustering.Genome biology · 2020Article
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2 authors.
Funding
Abstract
motivationBatch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (RNA-seq) data.
resultsWe present scBatch, a numerical algorithm for batch-effect correction on bulk and single-cell RNA-seq data with emphasis on improving both clustering and gene differential expression analysis. scBatch is not restricted by assumptions on the mechanism of batch-effect generation. As shown in simulations and real data analyses, scBatch outperforms benchmark batch-effect correction methods. AVAILABILITY AND IMPLEMENTATION: The R package is available at github.com/tengfei-emory/scBatch. The code to generate results and figures in this article is available at github.com/tengfei-emory/scBatch-paper-scripts. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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