# Benchmark principal component analysis (PCA) of scRNA-seq data in R

Principal component analysis (PCA) is frequently used for analysis of single-cell RNA-seq (scRNA-seq) data. We can use it to reduce the dimensionality of a large matrix with thousands of features (genes) to a smaller matrix with just a few factors (principal components). Since the latest scRNA-seq datasets include millions of cells, there is a need for efficient algorithms. Specifically, we need algorithms that work with sparse matrices instead of dense matrices. Here, we benchmark five implementations of singular value decomposition (SVD) and PCA.