ArticleComputational and structural biotechnology journal2025
Highly effective batch effect correction method for RNA-seq count data.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Investigating Metabolically Altered Pathways in Small Cell Lung Cancer: From RNA Sequencing Analysis to Seahorse-Based Functional Validation.Methods and protocols · 2026Article
- A Multi-Gene Signature Associated with 1-Year Survival in Patients with Stage I Liver Cancer: Integration of Preclinical and TCGA Data.Current issues in molecular biology · 2026Article
- Histone variant H2BE controls activity-dependent gene expression and homeostatic scaling.Cell reports · 2026Article
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- Multi-cohort transcriptomics integration for building and validating a diagnostic model of peripheral blood septic shock.Frontiers in immunology · 2026Article
- Peripheral Blood TCR Clonotype Diversity as a Biomarker for Colorectal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Autism spectrum disorder risk genes have convergent effects on transcription and neuronal firing patterns in primary neurons.Genome research · 2025Article
- Uncovering the Tumorigenic Blueprint of PFOS and PFOA Through Multi-Organ Transcriptomic Analysis of Biomarkers, Mechanisms, and Therapeutic Targets.Current issues in molecular biology · 2025Article
- Transcriptomic Analysis ofAnimals : an open access journal from MDPI · 2025Article
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- Evolutionary conservation of dopamine-mediated cellular plasticity in Arctic sponges (Porifera).Frontiers in molecular biosciences · 2025Article
- Integrative multidimensional analysis of age-associated synthetic lethal genes and development of a prognostic model in breast cancer.Frontiers in immunology · 2025Article
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Abstract
RNA sequencing (RNA-seq) has become a cornerstone of transcriptomics, providing detailed insights into gene expression across diverse biological conditions and sample types. However, RNA-seq data are often confounded by batch effects, systematic non-biological variations that compromise data reliability and obscure true biological differences. To address these challenges, we introduce ComBat-ref, a refined batch effect correction method designed to enhance the statistical power and reliability of differential expression analysis in RNA-seq data. Building on the principles of ComBat-seq, ComBat-ref employs a negative binomial model for count data adjustment but innovates by selecting a reference batch with the smallest dispersion, preserving count data for the reference batch, and adjusting other batches towards the reference batch. Our method demonstrated superior performance in both simulated environments and real-world datasets, including the growth factor receptor network (GFRN) data and NASA GeneLab transcriptomic datasets, significantly improving sensitivity and specificity compared to existing methods. By effectively mitigating batch effects while maintaining high detection power, ComBat-ref provides a robust solution for improving the accuracy and interpretability of RNA-seq data analyses.
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