doi: 10.1007/978-1-0716-1307-8_18.
Dimensionality Reduction of Single-Cell RNA-Seq Data
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- PMID: 33835451
- DOI: 10.1007/978-1-0716-1307-8_18
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Dimensionality Reduction of Single-Cell RNA-Seq Data
Methods Mol Biol.
2021.
Abstract
Dimensionality reduction is a crucial step in essentially every single-cell RNA-sequencing (scRNA-seq) analysis. In this chapter, we describe the typical dimensionality reduction workflow that is used for scRNA-seq datasets, specifically highlighting the roles of principal component analysis, t-distributed stochastic neighborhood embedding, and uniform manifold approximation and projection in this setting. We particularly emphasize efficient computation; the software implementations used in this chapter can scale to datasets with millions of cells.
Keywords: Dimensionality-reduction; Visualization; pca; scRNA-seq; t-SNE; umap.
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