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


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


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


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