kstreet13/scry: Small-Count Analysis Methods for High-Dimensional Data
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.
Getting started
Browse package contents
Package details |
|
|---|---|
| Bioconductor views | DimensionReduction GeneExpression Normalization PrincipalComponent RNASeq Sequencing SingleCell Software Transcriptomics |
| Maintainer | |
| License | Artistic-2.0 |
| Version | 1.12.0 |
| URL | https://bioconductor.org/packages/scry.html |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
|
