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Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.
Package details |
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Author | Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut] |
Bioconductor views | DimensionReduction GeneExpression Normalization PrincipalComponent RNASeq Sequencing SingleCell Software Transcriptomics |
Maintainer | Kelly Street <street.kelly@gmail.com> |
License | Artistic-2.0 |
Version | 1.2.0 |
URL | https://bioconductor.org/packages/scry.html |
Package repository | View on Bioconductor |
Installation |
Install the latest version of this package by entering the following in R:
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