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-4.4-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'))

Peer review:

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

Datasets:

On CRAN:

4.18 score 3 stars 8 scripts 104 downloads 1 exports 20 dependencies

Last updated 2 years agofrom:b25eac4ad6. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 18 2024
R-4.5-winNOTEOct 18 2024
R-4.5-linuxNOTEOct 18 2024
R-4.4-winNOTEOct 18 2024
R-4.4-macNOTEOct 18 2024
R-4.3-winNOTEOct 18 2024
R-4.3-macNOTEOct 18 2024

Exports:scISR

Dependencies:clustercodetoolscommonmarkdoParallelentropyFNNforeachimputeirlbaiteratorslatticemarkdownMatrixmatrixStatsmclustPINSPlusRcppRcppArmadilloRcppParallelxfun

scISR package manual

Rendered fromExample.Rmdusingknitr::knitron Oct 18 2024.

Last update: 2022-05-09
Started: 2021-01-21