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Understand CCA Following my last blog post on PCA projection and cell label transfer, we are going to talk about CCA.
In single-cell RNA-seq data integration using Canonical Correlation Analysis (CCA), we typically align two matrices representing different datasets, where both datasets have the same set of genes but different numbers of cells.
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Understand the example datasets We will use PBMC3k and PBMC10k data. We will project the PBMC3k data to the PBMC10k data and get the labels
library(Seurat) library(Matrix) library(irlba) # For PCA library(RcppAnnoy) # For fast nearest neighbor search library(dplyr) # Assuming the PBMC datasets (3k and 10k) are already normalized # and represented as sparse matrices # devtools::install_github('satijalab/seurat-data') library(SeuratData) #AvailableData() #InstallData("pbmc3k") pbmc3k<-UpdateSeuratObject(pbmc3k) pbmc3k@meta.