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Small-Count Analysis Methods for High-Dimensional Data

Bioconductor version: Release (3.19)

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")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

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


biocViews DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Sequencing, SingleCell, Software, Transcriptomics
Version 1.16.0
In Bioconductor since BioC 3.11 (R-4.0) (4 years)
License Artistic-2.0
Depends R (>= 4.0), stats, methods
Imports DelayedArray, glmpca (>= 0.2.0), Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular
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Suggests BiocGenerics, covr, DuoClustering2018, ggplot2, HDF5Array, knitr, markdown, rmarkdown, TENxPBMCData, testthat
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Follow Installation instructions to use this package in your R session.

Source Package scry_1.16.0.tar.gz
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macOS Binary (arm64) scry_1.16.0.tgz
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