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Dimensionality Reduction of Single-Cell RNA-Seq Data.

Authors :
Linderman GC
Source :
Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2021; Vol. 2284, pp. 331-342.
Publication Year :
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.

Details

Language :
English
ISSN :
1940-6029
Volume :
2284
Database :
MEDLINE
Journal :
Methods in molecular biology (Clifton, N.J.)
Publication Type :
Academic Journal
Accession number :
33835451
Full Text :
https://doi.org/10.1007/978-1-0716-1307-8_18