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Dimensionality Reduction of Single-Cell RNA-Seq Data.
- 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.
- Subjects :
- Algorithms
Animals
Data Analysis
Datasets as Topic statistics & numerical data
Humans
Principal Component Analysis
Software
Computational Biology methods
RNA-Seq methods
RNA-Seq standards
RNA-Seq statistics & numerical data
Single-Cell Analysis methods
Single-Cell Analysis standards
Single-Cell Analysis statistics & numerical data
Subjects
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