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cudacellr: GPU-Aware Single-Cell Analysis Workflows
Provides single-cell analysis primitives for sparse count matrices, including library-size normalization, log-normalization, highly variable feature selection, principal component analysis, and nearest-neighbour construction. Integrates the 'cudaverse' numerical and sparse layers while preserving matrix identifiers and recording the computation backend for each stage. Optional adapters preserve native 'SingleCellExperiment' and 'SeuratObject' version 5 object structures.
Getting started
Browse package contents
Package details |
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| Author | Yaoxiang Li [aut, cre] |
| Maintainer | Yaoxiang Li <liyaoxiang@outlook.com> |
| License | MIT + file LICENSE |
| Version | 0.4.0 |
| URL | https://cudaverse.github.io/cudacellr/ https://github.com/cudaverse/cudacellr |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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