Package: SCArray.sat
Type: Package
Title: Large-scale single-cell RNA-seq data analysis using GDS files and
        Seurat
Version: 1.11.0
Date: 2025-03-24
Depends: methods, SCArray (>= 1.13.1), SeuratObject (>= 5.0), Seurat (>= 5.0)
Imports: S4Vectors, utils, stats, BiocGenerics, BiocParallel, gdsfmt,
        DelayedArray, BiocSingular, SummarizedExperiment, Matrix
Suggests: future, RUnit, knitr, markdown, rmarkdown, BiocStyle
Authors@R: c(person("Xiuwen", "Zheng", role=c("aut", "cre"), email=
        "xiuwen.zheng@abbvie.com", comment=c(ORCID="0000-0002-1390-0708")),
        person("Seurat contributors", role="ctb",
        comment="for the classes and methods defined in Seurat"))
Description: Extends the Seurat classes and functions to support Genomic Data
        Structure (GDS) files as a DelayedArray backend for data representation.
        It relies on the implementation of GDS-based DelayedMatrix in the
        SCArray package to represent single cell RNA-seq data. The common
        optimized algorithms leveraging GDS-based and single cell-specific
        DelayedMatrix (SC_GDSMatrix) are implemented in the SCArray package.
        SCArray.sat introduces a new SCArrayAssay class (derived from the
        Seurat Assay), which wraps raw counts, normalized expressions and
        scaled data matrix based on GDS-specific DelayedMatrix. It is designed
        to integrate seamlessly with the Seurat package to provide common data
        analysis in the SeuratObject-based workflow. Compared with Seurat,
        SCArray.sat significantly reduces the memory usage without downsampling
        and can be applied to very large datasets.
License: GPL-3
VignetteBuilder: knitr
BugReports: https://github.com/AbbVie-ComputationalGenomics/SCArray/issues
biocViews: DataRepresentation, DataImport, SingleCell, RNASeq
git_url: https://git.bioconductor.org/packages/SCArray.sat
git_branch: devel
git_last_commit: 10b697d
git_last_commit_date: 2025-10-29
Repository: Bioconductor 3.23
