DOI: 10.18129/B9.bioc.scry  

This package is for version 3.16 of Bioconductor; for the stable, up-to-date release version, see scry.

Small-Count Analysis Methods for High-Dimensional Data

Bioconductor version: 3.16

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.

Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut]

Maintainer: Kelly Street <street.kelly at>

Citation (from within R, enter citation("scry")):


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HTML R Script Overview of Scry Methods
HTML R Script Scry Methods For Larger Datasets
PDF   Reference Manual


biocViews DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Sequencing, SingleCell, Software, Transcriptomics
Version 1.10.0
In Bioconductor since BioC 3.11 (R-4.0) (3 years)
License Artistic-2.0
Depends R (>= 4.0), stats, methods
Imports DelayedArray, glmpca (>= 0.2.0), HDF5Array, Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular
Suggests markdown, BiocGenerics, covr, DuoClustering2018, ggplot2, knitr, rmarkdown, TENxPBMCData, testthat
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