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>.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.2.0) |
| Imports: | methods, Rcpp (≥ 0.12.19) |
| LinkingTo: | Rcpp, BH |
| Published: | 2021-09-06 |
| Author: | Prashant Gupta [aut, cre], Aashi Jindal [aut], Jayadeva [aut], Debarka Sengupta [aut] |
| Maintainer: | Prashant Gupta <prashant10991 at gmail.com> |
| BugReports: | https://github.com/princethewinner/FiRE/issues |
| License: | GPL-3 |
| URL: | https://github.com/princethewinner/FiRE |
| NeedsCompilation: | yes |
| CRAN checks: | FiRE results |
| Reference manual: | FiRE.pdf |
| Package source: | FiRE_1.0.1.tar.gz |
| Windows binaries: | r-devel: FiRE_1.0.1.zip, r-release: FiRE_1.0.1.zip, r-oldrel: FiRE_1.0.1.zip |
| macOS binaries: | r-release (arm64): FiRE_1.0.1.tgz, r-oldrel (arm64): FiRE_1.0.1.tgz, r-release (x86_64): FiRE_1.0.1.tgz, r-oldrel (x86_64): FiRE_1.0.1.tgz |
| Old sources: | FiRE archive |
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