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
Author:	Timothy Barry [aut, cre], Eugene Katsevich [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:

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