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ondisc: Fast, Universal, and Intuitive Computing on Large-Scale Single-Cell Data

Single-cell datasets are growing in size, posing challenges as well as opportunities for biology researchers. 'ondisc' (short for "on-disk single cell") enables users to easily and efficiently analyze large-scale single-cell data. 'ondisc' makes computing on large-scale single-cell data FUN: Fast, Universal, and iNtuitive.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: readr, methods, magrittr, rhdf5, data.table, Matrix, Rcpp, crayon, dplyr
LinkingTo: Rcpp, Rhdf5lib
Suggests: testthat, knitr, rmarkdown, covr
Published: 2021-03-05
DOI: 10.32614/CRAN.package.ondisc
Author: Timothy Barry ORCID iD [aut, cre], Eugene Katsevich ORCID iD [ths], Kathryn Roeder [ths]
Maintainer: Timothy Barry <tbarry2 at andrew.cmu.edu>
License: MIT + file LICENSE
URL: https://timothy-barry.github.io/ondisc/
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README
CRAN checks: ondisc results

Documentation:

Reference manual: ondisc.pdf
Vignettes: Tutorial 1: Using the 'ondisc_matrix' class
Tutorial 2: Using 'metadata_ondisc_matrix' and 'multimodal_ondisc_matrix'

Downloads:

Package source: ondisc_1.0.0.tar.gz
Windows binaries: r-devel: ondisc_1.0.0.zip, r-release: ondisc_1.0.0.zip, r-oldrel: ondisc_1.0.0.zip
macOS binaries: r-release (arm64): ondisc_1.0.0.tgz, r-oldrel (arm64): ondisc_1.0.0.tgz, r-release (x86_64): ondisc_1.0.0.tgz, r-oldrel (x86_64): ondisc_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=ondisc to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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