ArticleComputational and structural biotechnology journal2020
A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
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- USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.Briefings in bioinformatics · 2026Article
- Machine learning-based assessment of the healthy human gut mycobiota landscape using ITS1 DNA metabarcoding data.BioData mining · 2026Article
- Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation.Renal failure · 2025Article
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.Laboratory investigation; a journal of technical methods and pathology · 2024Article
- Review
- Single-cell transcriptomics reveal a hyperacute cytokine and immune checkpoint axis after cardiac arrest in patients with poor neurological outcome.Med (New York, N.Y.) · 2023Article
- Genome-wide mapping of cancer dependency genes and genetic modifiers of chemotherapy in high-risk hepatoblastoma.Nature communications · 2023Article
- The shaky foundations of simulating single-cell RNA sequencing data.Genome biology · 2023Article
- A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer.Journal of clinical medicine · 2022Article
- Cryobanking of Human Distal Lung Epithelial Cells for Preservation of Their Phenotypic and Functional Characteristics.American journal of respiratory cell and molecular biology · 2022Article
- Expression Analysis in a Dispersal-Fecundity Polyphenism Identifies Growth Regulators and Effectors.Integrative and comparative biology · 2022Article
- geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq.Genome biology · 2021Article
- scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies.Nature communications · 2021Article
- Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer.Nucleic acids research · 2021Article
- Single-cell RNA Sequencing Reveals Sexually Dimorphic Transcriptome and Type 2 Diabetes Genes in Mouse Islet β Cells.Genomics, proteomics & bioinformatics · 2021Article
- Single-cell transcriptomics: a novel precision medicine technique in nephrology.The Korean journal of internal medicine · 2021Review
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8 authors.
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Abstract
Accounting for batch effects, especially latent batch effects, in differential expression (DE) analysis is critical for identifying true biological effects. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for quantifying cell-to-cell variation in transcript abundance and characterizing cellular dynamics. Although many scRNA-seq DE analysis methods accommodate known batch variables, their performance has not been systematically evaluated. Moreover, the challenge of accounting for latent batch variables in scRNA-seq DE analysis is largely unmet. In contrast, many methods have been developed to account for batch variables (either known or latent) in other high-dimensional data, especially bulk RNA-seq. We extensively evaluate 11 methods for batch variables in different scRNA-seq DE analysis scenarios, with a primary focus on latent batch variables. We demonstrate that for known batch variables, incorporating them as covariates into a regression model outperformed approaches using a batch-corrected matrix. For latent batches, fixed effects models have inflated FDRs, whereas aggregation-based methods and mixed effects models have significant power loss. Surrogate variable based methods generally control the FDR well while achieving good power with small group effects. However, their performance (except that of SVA) deteriorated substantially in scenarios involving large group effects and/or group label impurity. In these settings, SVA achieves relatively good performance despite an occasionally inflated FDR (up to 0.2). Finally we make the following recommendations for scRNA-seq DE analysis: 1) incorporate known batch variables instead of using batch-corrected data; and 2) employ SVA for latent batch correction. However, better methods are still needed to fully unleash the power of scRNA-seq.
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