Algorithms and Data Structures for Large Single-Cell Expression Matrices R package ondisc version 1.3.5 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. Software Depends: R (>= 4.1.0) Imports: crayon, data.table, methods, Rcpp, Rhdf5lib, dplyr, readr, Matrix LinkingTo: Rcpp, Rhdf5lib Suggests: sessioninfo, knitr, R.utils, rmarkdown, testthat (>= 3.0.0) Timothy Barry <tbarry@hsph.harvard.edu> Comprehensive R Archive Network (CRAN) Timothy Barry [aut, cre] (ORCID: <https://orcid.org/0000-0002-4356-627X>), Songcheng Dai [ctb], Yixuan Qiu [ctb], Eugene Katsevich [aut, ths] MIT + file LICENSE (https://CRAN.R-project.org/package=ondisc/LICENSE) 2026-06-17 application/tgz https://CRAN.R-project.org/package=ondisc doi:10.32614/CRAN.package.ondisc