https://csglab.r-universe.devRecent package updates in csglabR-universehttps://github.com/csglab.pnghttps://csglab.r-universe.devTue, 09 Jun 2026 21:45:24 GMTarsham.mikaeilinamini@mail.mcgill.ca (Arsham Mikaeili Namini)A memory-efficient implementation for integrating gene expression data from single-cell RNA sequencing experiments. Uses a C++ backend with thin R wrappers to enable analysis of large-scale single-cell datasets. The package supports multiple data modalities including count matrices, paired data (splicing, RNA velocity, CITE-seq), and binary indicators. It implements a latent variable model with block coordinate descent optimization for dimensionality reduction and batch effect correction. Core algorithms are described in Madrigal et al. (2024) <doi:10.1038/s41467-024-50963-0>.https://github.com/r-universe/csglab/actions/runs/27239313121Tue, 09 Jun 2026 21:45:24 GMTgedi22.3.5successhttps://csglab.r-universe.devhttps://github.com/csglab/gedi2arsham.mikaeilinamini@mail.mcgill.ca (Arsham Mikaeili Namini)Provides analysis of high-dimensional single-cell splicing data. Offers a framework to extract and work with ratio-based data structures derived from single-cell RNA sequencing experiments. Provides both a modern 'R6' object-oriented interface and direct matrix manipulation functions. Core functionalities are implemented in 'C++' via 'Rcpp' to ensure high performance and scalability on large datasets.https://github.com/r-universe/csglab/actions/runs/27239316583Tue, 09 Jun 2026 19:32:40 GMTsplikit2.3.2successhttps://csglab.r-universe.devhttps://github.com/csglab/splikitmethods.Rmdmethods.htmlMethods Overview2026-05-09 02:07:422026-06-09 19:32:40splikit_manual.Rmdsplikit_manual.htmlSplikit Manual for Single-Cell Splicing Analysis2025-08-10 05:06:442026-06-09 19:32:40STARsolo_guide.RmdSTARsolo_guide.htmlSTARsolo Processing Guide for Splicing Analysis2025-08-10 05:06:442025-08-10 05:06:44arsham.mikaeilinamini@mail.mcgill.ca (Arsham Mikaeili Namini)A memory-efficient implementation for integrating gene expression data from single-cell RNA sequencing experiments. Uses a C++ backend with thin R wrappers to enable analysis of large-scale single-cell datasets. The package supports multiple data modalities including count matrices, paired data (splicing, RNA velocity, CITE-seq), and binary indicators. It implements a latent variable model with block coordinate descent optimization for dimensionality reduction and batch effect correction. Core algorithms are described in Madrigal et al. (2024) <doi:10.1038/s41467-024-50963-0>.https://github.com/r-universe/cran/actions/runs/26093514790Tue, 19 May 2026 08:41:47 GMTgedi22.3.4successhttps://cran.r-universe.devhttps://github.com/cran/gedi2arsham.mikaeilinamini@mail.mcgill.ca (Arsham Mikaeili Namini)Provides analysis of high-dimensional single-cell splicing data. Offers a framework to extract and work with ratio-based data structures derived from single-cell RNA sequencing experiments. Provides both a modern 'R6' object-oriented interface and direct matrix manipulation functions. Core functionalities are implemented in 'C++' via 'Rcpp' to ensure high performance and scalability on large datasets.https://github.com/r-universe/cran/actions/runs/25798160294Wed, 13 May 2026 10:48:18 GMTsplikit2.3.1successhttps://cran.r-universe.devhttps://github.com/cran/splikitmethods.Rmdmethods.htmlMethods Overview2026-05-13 10:48:182026-05-13 10:48:18splikit_manual.Rmdsplikit_manual.htmlSplikit Manual for Single-Cell Splicing Analysis2026-05-13 10:48:182026-05-13 10:48:18STARsolo_guide.RmdSTARsolo_guide.htmlSTARsolo Processing Guide for Splicing Analysis2026-05-13 10:48:182026-05-13 10:48:18