This package is considered a duplicate. The official version of this package is found at:https://mohmedsoudy.r-universe.dev/ScRNAIMM

Package: ScRNAIMM 0.1

Mohamed Soudy

ScRNAIMM: Performing Single-Cell RNA-Seq Imputation by Using Mean/Median Imputation

Performing single-cell imputation in a way that preserves the biological variations in the data. The package clusters the input data to do imputation for each cluster, and do a distribution check using the Anderson-Darling normality test to impute dropouts using mean or median (Yazici, B., & Yolacan, S. (2007) <doi:10.1080/10629360600678310>).

Authors:Mohamed Soudy [aut, cre], Sascha Jung [aut], Antonio DEL SOL [aut]

ScRNAIMM_0.1.tar.gz
ScRNAIMM_0.1.zip(r-4.5)ScRNAIMM_0.1.zip(r-4.4)ScRNAIMM_0.1.zip(r-4.3)
ScRNAIMM_0.1.tgz(r-4.5-any)ScRNAIMM_0.1.tgz(r-4.4-any)ScRNAIMM_0.1.tgz(r-4.3-any)
ScRNAIMM_0.1.tar.gz(r-4.5-noble)ScRNAIMM_0.1.tar.gz(r-4.4-noble)
ScRNAIMM_0.1.tgz(r-4.4-emscripten)ScRNAIMM_0.1.tgz(r-4.3-emscripten)
ScRNAIMM.pdf |ScRNAIMM.html✨
ScRNAIMM/json (API)

# Install 'ScRNAIMM' in R:

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

On CRAN:

archived packages r-universe

1.70 score 5 stars 161 downloads 7 exports 60 dependencies

Last updated 6 days agofrom:34d637c7c1 (on package/ScRNAIMM). Checks:9 OK. Indexed: no.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 14 2025
R-4.5-win	OK	Mar 16 2025
R-4.5-mac	OK	Mar 16 2025
R-4.5-linux	OK	Mar 14 2025
R-4.4-win	OK	Mar 16 2025
R-4.4-mac	OK	Mar 16 2025
R-4.4-linux	OK	Mar 14 2025
R-4.3-win	OK	Mar 16 2025
R-4.3-mac	OK	Mar 16 2025

Exports:cluster_cells evaluate_clustering filter_ScRNA prepare_dataset run_pipeline ScRNA_imp_MM scRNA_MMI

Dependencies:BH bit bit64 callr cli cluster codetools colorspace coro cpp11 desc doParallel dplyr dqrng fansi farver FNN foreach generics glue igraph irlba iterators jsonlite labeling lattice lifecycle magrittr Matrix matrixStats mclust munsell nortest pillar pkgconfig processx ps R6 RColorBrewer Rcpp RcppAnnoy RcppArmadillo RcppEigen RcppParallel RcppProgress RhpcBLASctl rlang RSpectra safetensors scales scDHA sitmo tibble tidyselect torch utf8 uwot vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Perform cell clustering based on scDHA method	cluster_cells
Evaluate the clustering if you have the original labels	evaluate_clustering
Remove genes which are not expressed in at least one cell	filter_ScRNA
Prepare the data set for the imputation	prepare_dataset
Run the main pipeline for ScRNAIMM	run_pipeline
Perform ScRNA-seq imputation using mean/Median	ScRNA_imp_MM
Performs a distribution check for the data	scRNA_MMI