scISR: Single-Cell Imputation using Subspace Regression (original) (raw)
Provides an imputation pipeline for single-cell RNA sequencing data. The 'scISR' method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <doi:10.1038/s41598-022-06500-4>).
| Version: | 0.1.1 | |
|---|---|---|
| Depends: | R (≥ 3.4) | |
| Imports: | cluster, entropy, stats, utils, parallel, irlba, PINSPlus, matrixStats, markdown | |
| Suggests: | testthat, knitr, mclust | |
| Published: | 2022-06-30 | |
| DOI: | 10.32614/CRAN.package.scISR | |
| Author: | Duc Tran [aut, cre], Bang Tran [aut], Hung Nguyen [aut], Tin Nguyen [fnd] | |
| Maintainer: | Duc Tran | |
| BugReports: | https://github.com/duct317/scISR/issues | |
| License: | LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL] |
| URL: | https://github.com/duct317/scISR | |
| NeedsCompilation: | no | |
| Citation: | scISR citation info | |
| Materials: | README | |
| CRAN checks: | scISR results |
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