Package: FiRE 1.0.1

FiRE: Finder of Rare Entities (FiRE)

The algorithm assigns rareness/ outlierness score to every sample in voluminous datasets. The algorithm makes multiple estimations of the proximity between a pair of samples, in low-dimensional spaces. To compute proximity, FiRE uses Sketching, a variant of locality sensitive hashing. For more details: Jindal, A., Gupta, P., Jayadeva and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data. Nature Communications, 9(1), p.4719. <doi:10.1038/s41467-018-07234-6>.

Authors:Prashant Gupta [aut, cre], Aashi Jindal [aut], Jayadeva [aut], Debarka Sengupta [aut]

FiRE_1.0.1.tar.gz
FiRE_1.0.1.zip(r-4.7)FiRE_1.0.1.zip(r-4.6)FiRE_1.0.1.zip(r-4.5)
FiRE_1.0.1.tgz(r-4.6-x86_64)FiRE_1.0.1.tgz(r-4.6-arm64)FiRE_1.0.1.tgz(r-4.5-x86_64)FiRE_1.0.1.tgz(r-4.5-arm64)
FiRE_1.0.1.tar.gz(r-4.7-arm64)FiRE_1.0.1.tar.gz(r-4.7-x86_64)FiRE_1.0.1.tar.gz(r-4.6-arm64)FiRE_1.0.1.tar.gz(r-4.6-x86_64)
FiRE_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
FiRE/json (API)

# Install 'FiRE' in R:
install.packages('FiRE', repos = c('https://princethewinner.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/princethewinner/fire/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • sample_data - Preprocessed 293T–Jurkat scRNA-seq dataset
  • sample_label - Labels of preprocessed 293T–Jurkat scRNA-seq dataset

On CRAN:

Conda:

cpp

1.30 score 9 scripts 198 downloads 2 mentions 1 exports 2 dependencies

Last updated from:56eaca7551. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK126
linux-devel-x86_64OK127
source / vignettesOK197
linux-release-arm64OK141
linux-release-x86_64OK126
macos-release-arm64OK166
macos-release-x86_64OK250
macos-oldrel-arm64OK159
macos-oldrel-x86_64OK327
windows-develOK157
windows-releaseOK139
windows-oldrelOK121
wasm-releaseOK108

Exports:FiRE

Dependencies:BHRcpp