ondisc: Algorithms and Data Structures for Large Single-Cell Expression Matrices

Single-cell datasets are growing in size, posing challenges as well as opportunities for genomics researchers. 'ondisc' is an R package that facilitates analysis of large-scale single-cell data out-of-core on a laptop or distributed across tens to hundreds of processors on a cluster or cloud. In both of these settings, 'ondisc' requires only a few gigabytes of memory, even if the input data are tens of gigabytes in size. 'ondisc' mainly is oriented toward single-cell CRISPR screen analysis, but also can be used for single-cell differential expression and single-cell co-expression analyses. 'ondisc' is powered by several new, efficient algorithms for manipulating and querying large, sparse expression matrices.

Package details

AuthorTimothy Barry [aut, cre] (ORCID: <https://orcid.org/0000-0002-4356-627X>), Songcheng Dai [ctb], Yixuan Qiu [ctb], Eugene Katsevich [aut, ths]
Bioconductor views CRISPR DataImport DifferentialExpression SingleCell
MaintainerTimothy Barry <tbarry@hsph.harvard.edu>
LicenseMIT + file LICENSE
Version1.3.5
URL https://timothy-barry.github.io/ondisc/ https://timothy-barry.github.io/sceptre-book/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("ondisc")

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ondisc documentation built on June 17, 2026, 5:06 p.m.