Installation | Entropy Analysis | Using SCEnt to Quantify Homogeneity and Heterogeneity | Using SCEnt For Feature Selection | Tidy Implementation of Feature Selection
Package: SCEnt 0.1.0
SCEnt: Single Cell Entropy Analysis of Gene Heterogeneity in Cell Populations
Analyse single cell RNA sequencing data using entropy to calculate heterogeneity and homogeneity of genes amongst the cell population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia and Ben D. MacArthur (2020) <doi:10.1101/2020.10.01.322255>.
Authors:
SCEnt_0.1.0.tar.gz
SCEnt_0.1.0.zip(r-4.7-any)SCEnt_0.1.0.zip(r-4.6-any)SCEnt_0.1.0.zip(r-4.5-any)
SCEnt_0.1.0.tgz(r-4.6-any)SCEnt_0.1.0.tgz(r-4.5-any)
SCEnt_0.1.0.tar.gz(r-4.7-any)SCEnt_0.1.0.tar.gz(r-4.6-any)
SCEnt_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SCEnt/json (API)
| # Install 'SCEnt' in R: |
| install.packages('SCEnt', repos = c('https://hwarden162.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hwarden162/scent/issues
Last updated from:f99eb8ea89. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 132 | ||
| source / vignettes | OK | 215 | ||
| linux-release | OK | 149 | ||
| macos-release | OK | 137 | ||
| macos-oldrel | OK | 182 | ||
| windows-devel | OK | 65 | ||
| windows-release | OK | 65 | ||
| windows-oldrel | OK | 63 | ||
| wasm-release | OK | 87 |
Exports:gene_countsgene_hetgene_homrm_low_countsrm_low_counts_tidyscent_selectscent_select_tidy
Dependencies:clientropygluelifecyclemagrittrpillarpkgconfigrlangtibbleutf8vctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Find the Number of Times Each Gene Has Been Expressed | gene_counts |
| Find the Heterogeneity of a Gene Within a Population | gene_het |
| Find the Homogeneity of a Gene Within a Population | gene_hom |
| Normalise Counts into a Distribution | normalise |
| Remove Lowly Expressed Genes From Expression Data | rm_low_counts |
| Tidy Wrapper To Remove Lowly Expressed Genes From Expression Data | rm_low_counts_tidy |
| Feature Selection by Gene Heterogeneity | scent_select |
| A Tidy Wrapper for Feature Selection by Heterogeneity | scent_select_tidy |