ArticleScientific reports2023
Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.Genome biology · 2026Article
- Integrated Single-Cell and Bulk RNA Sequencing Identifies Macrophage Heterogeneity and Mitophagy-Related Biomarkers in Idiopathic Pulmonary Fibrosis.International journal of molecular sciences · 2026Article
- Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Single cell RNA sequencing of haematopoietic cells in fresh and frozen human atheroma tissue.Cardiovascular research · 2025Article
- STForte: tissue context-specific encoding and consistency-aware spatial imputation for spatially resolved transcriptomics.Briefings in bioinformatics · 2025Article
- Interpretable single-cell factor decomposition using sciRED.Nature communications · 2025Article
- Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications.Bioengineering (Basel, Switzerland) · 2025Review
- Single-cell RNA seq data analysis reveals molecular markers and possible treatment targets for laryngeal squamous cell carcinoma (LSCC): an in-silico approach.In silico pharmacology · 2025Article
- Interpretable single-cell factor decomposition using sciRED.bioRxiv : the preprint server for biology · 2024Article
- Mcadet: A feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection.PLoS computational biology · 2024Article
- Interpretable single-cell factor decomposition using sciRED.Research square · 2024Article
- Single-cell RNA sequencing reveals special basal cells and fibroblasts in idiopathic pulmonary fibrosis.Scientific reports · 2024Article
- OASIS: An interpretable, finite-sample valid alternative to Pearson'sProceedings of the National Academy of Sciences of the United States of America · 2024Article
- Systematic analysis on the horse-shoe-like effect in PCA plots of scRNA-seq data.Bioinformatics advances · 2024Article
- OASIS: An interpretable, finite-sample valid alternative to Pearson'sbioRxiv : the preprint server for biology · 2023Article
- Next-Generation Morphometry for pathomics-data mining in histopathology.Nature communications · 2023Article
- FastRNA: An efficient solution for PCA of single-cell RNA-sequencing data based on a batch-accounting count model.American journal of human genetics · 2022Article
- Anti-CAIX BBζ CAR4/8 T cells exhibit superior efficacy in a ccRCC mouse model.Molecular therapy oncolytics · 2022Article
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2 authors.
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Abstract
Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore, it is often coupled with log-transformation in scRNAseq applications, which can distort the data and obscure meaningful variation. We describe correspondence analysis (CA), a count-based alternative to PCA. CA is based on decomposition of a chi-squared residual matrix, avoiding distortive log-transformation. To address overdispersion and high sparsity in scRNAseq data, we propose five adaptations of CA, which are fast, scalable, and outperform standard CA and glmPCA, to compute cell embeddings with more performant or comparable clustering accuracy in 8 out of 9 datasets. In particular, we find that CA with Freeman-Tukey residuals performs especially well across diverse datasets. Other advantages of the CA framework include visualization of associations between genes and cell populations in a "CA biplot," and extension to multi-table analysis; we introduce corralm for integrative multi-table dimension reduction of scRNAseq data. We implement CA for scRNAseq data in corral, an R/Bioconductor package which interfaces directly with single cell classes in Bioconductor. Switching from PCA to CA is achieved through a simple pipeline substitution and improves dimension reduction of scRNAseq datasets.
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