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.

Package details

AuthorKelly 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
MaintainerKelly Street <>
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


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scry documentation built on Nov. 8, 2020, 5:16 p.m.