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ondisc: large-scale computing on single-cell data

Single-cell datasets are growing in size, posing challenges as well as opportunities to biology researchers. ondisc (short for “on-disk single cell”) is an R package that enables users to easily and efficiently analyze large-scale single-cell data. ondisc makes computing on large-scale single-cell data FUN:

  • Fast: ondisc is powered by several novel, highly efficient algorithms and data structures. All low-level code is written in C++ or C for maximum performance.
  • Universal: ondisc runs on all platforms, from laptops to supercomputers. ondisc works seamlessly when the size of the data exceeds the amount of available memory.
  • Ntuitive: ondisc leverages ideas from functional programming, making it simple for R users users to pick up and incorporate into their programs.

Take a look at the tutorials on the package website.

Installation

You can install the development version from GitHub with:

install.packages("devtools")
devtools::install_github("timothy-barry/ondisc")

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Version

Install

install.packages('ondisc')

Monthly Downloads

57

Version

1.0.0

License

MIT + file LICENSE

Maintainer

Timothy Barry

Last Published

March 5th, 2021

Functions in ondisc (1.0.0)

show

Print basic information to the console
dim

Get dimension
ondisc

ondisc: A package for out-of-memory computing on single-cell data
create_ondisc_matrix_from_mtx

Create an ondisc_matrix from a .mtx file.
extract-odm

Pull a submatrix into memory using the [[ operator.
metadata_ondisc_matrix

metadata_ondisc_matrix class
head,ondisc_matrix-method

head
ondisc_matrix

ondisc_matrix class
multimodal_ondisc_matrix

multimodal_ondisc_matrix class
get-names

Get cell barcodes, feature names, and feature IDs
subset-odm

Subset using the [ operator.