Package: scISR 0.1.1

Duc Tran

scISR: Single-Cell Imputation using Subspace Regression

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>).

Authors:Duc Tran [aut, cre], Bang Tran [aut], Hung Nguyen [aut], Tin Nguyen [fnd]

scISR_0.1.1.tar.gz
scISR_0.1.1.zip(r-4.5)scISR_0.1.1.zip(r-4.4)scISR_0.1.1.zip(r-4.3)
scISR_0.1.1.tgz(r-4.5-any)scISR_0.1.1.tgz(r-4.4-any)scISR_0.1.1.tgz(r-4.3-any)
scISR_0.1.1.tar.gz(r-4.5-noble)scISR_0.1.1.tar.gz(r-4.4-noble)
scISR_0.1.1.tgz(r-4.4-emscripten)scISR_0.1.1.tgz(r-4.3-emscripten)
scISR.pdf |scISR.html✨
scISR/json (API)

# Install 'scISR' in R:

install.packages('scISR', repos = c('https://duct317.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/duct317/scisr/issues

Datasets:

Goolam - Goolam

On CRAN:

4.18 score 3 stars 8 scripts 145 downloads 1 exports 20 dependencies

Last updated 3 years agofrom:b25eac4ad6. Checks:1 OK, 7 NOTE. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 15 2025
R-4.5-win	NOTE	Feb 15 2025
R-4.5-mac	NOTE	Feb 15 2025
R-4.5-linux	NOTE	Feb 15 2025
R-4.4-win	NOTE	Feb 15 2025
R-4.4-mac	NOTE	Feb 15 2025
R-4.3-win	NOTE	Dec 17 2024
R-4.3-mac	NOTE	Dec 17 2024