Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data.

Hsu, Lauren L; Culhane, Aedín C

dc.contributor.author	Hsu, Lauren L
dc.contributor.author	Culhane, Aedín C
dc.date.accessioned	2024-07-23T08:04:11Z
dc.date.available	2024-07-23T08:04:11Z
dc.date.issued	2023-01-21
dc.identifier.pmid	36681709
dc.identifier.doi	10.1038/s41598-022-26434-1
dc.identifier.uri	http://hdl.handle.net/10147/642427
dc.description	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.	en_US
dc.language.iso	en	en_US
dc.rights	© 2023. The Author(s).
dc.rights	Attribution 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	single cell	en_US
dc.subject	pipeline	en_US
dc.title	Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data.	en_US
dc.type	Article	en_US
dc.type	Other	en_US
dc.identifier.eissn	2045-2322
dc.identifier.journal	Scientific reports	en_US
dc.source.journaltitle	Scientific reports
dc.source.volume	13
dc.source.issue	1
dc.source.beginpage	1197
dc.source.endpage
refterms.dateFOA	2024-07-23T08:04:14Z
dc.source.country	United States
dc.source.country	England