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RCA (Reference Component Analysis) is a computational approach for robust clustering and cell type annotation of single cell RNA sequencing data (scRNAseq). RCA can also substantially improve clustering accuracy. The updated version RCAv2 is the first algorithm to combine reference projection (batch effect robustness) with graph-based clustering (scalability).
RCAv2 requires R version >= 3.5.0, and some other prerequisite packages in R (dplyr, ggplot2, etc.). More info can be found here:https://github.com/prabhakarlab/RCAv2#install-the-rca-r-package and https://github.com/prabhakarlab/RCAv2
RCA2 is freely available for academic purposes. Commercial usage requires a license.
The original RCA(v1): Li, Huipeng, et al. "Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors." Nature genetics 49.5 (2017): 708-718. DOI: 10.1038/ng.3818. PMID: 28319088 RCA(v2): Schmidt, Florian, et al. "RCA2: a scalable supervised clustering algorithm that reduces batch effects in scRNA-seq data." Nucleic Acids Research 49.15 (2021): 8505-8519. DOI: 10.1093/nar/gkab632. PMID: 34320202