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Analysis of single-cell RNA sequencing data can be simple and clear with the right utility functions. This package collects such functions, aiming to fulfill the following criteria: code clarity over performance (i.e. plain R code instead of C code), most important analysis steps over completeness (analysis 'by hand', not automated integration etc.), emphasis on quantitative visualization (intensity-coded color scale, etc.).
| Version: | 0.1.0 |
| Imports: | ggplot2, Matrix, scales, assertthat, dplyr, viridis, viridisLite, methods |
| Suggests: | testthat, tibble |
| Published: | 2020-06-25 |
| DOI: | 10.32614/CRAN.package.scUtils |
| Author: | Felix Frauhammer [aut, cre], Simon Anders [ctb] (Simon Anders wrote the colVars_spm function.) |
| Maintainer: | Felix Frauhammer <felixwertek at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | scUtils results |
| Reference manual: | scUtils.pdf |
| Package source: | scUtils_0.1.0.tar.gz |
| Windows binaries: | r-devel: scUtils_0.1.0.zip, r-release: scUtils_0.1.0.zip, r-oldrel: scUtils_0.1.0.zip |
| macOS binaries: | r-release (arm64): scUtils_0.1.0.tgz, r-oldrel (arm64): scUtils_0.1.0.tgz, r-release (x86_64): scUtils_0.1.0.tgz, r-oldrel (x86_64): scUtils_0.1.0.tgz |
| Reverse imports: | cellpypes |
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These binaries (installable software) and packages are in development.
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