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README.md
Large-scale single-cell RNA-seq data analysis using GDS files and Seurat ==== ![GPLv3](http://www.gnu.org/graphics/gplv3-88x31.png) [GNU General Public License, GPLv3](http://www.gnu.org/copyleft/gpl.html) ## Features The package extends the [Seurat](https://cran.r-project.org/web/packages/Seurat/index.html) classes and functions to support Genomic Data Structure ([GDS](http://www.bioconductor.org/packages/gdsfmt)) files as a DelayedArray backend for data representation. It relies on the implementation of GDS-based DelayedMatrix in the [SCArray](http://www.bioconductor.org/packages/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. This package 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 and can be applied to very large datasets. ![**Figure 1**: Overview of the SCArray framework.](vignettes/scarray_sat.svg) ## Bioconductor v0.99.0 Package News: [NEWS](./NEWS) ## Package Maintainer [Xiuwen Zheng](xiuwen.zheng@abbvie.com) ## Installation * Requires [SCArray](http://www.bioconductor.org/packages/SCArray/) (≥ v1.7.13), [SeuratObject](https://cran.r-project.org/package=SeuratObject) (≥ v4.0), [Seurat](https://cran.r-project.org/package=Seurat) (≥ v4.0) * Bioconductor repository ```R if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("SCArray.sat") ``` ## Examples ```R suppressPackageStartupMessages({ library(Seurat) library(SCArray.sat) }) # an input GDS file with raw counts fn <- system.file("extdata", "example.gds", package="SCArray") fn # create a Seurat object from the GDS file d <- scNewSeuratGDS(fn) class(GetAssay(d)) # SCArrayAssay, derived from Assay d <- NormalizeData(d) d <- FindVariableFeatures(d) d <- ScaleData(d) d <- RunPCA(d) DimPlot(d, reduction="pca") d <- RunUMAP(d, dims=1:50) # use all PCs calculated by RunPCA() DimPlot(d, reduction="umap") saveRDS(d, "work.rds") # save the Seurat object without raw count data # check the internal data matrices GetAssayData(d, "counts") # SC_GDSMatrix path(GetAssayData(d, "counts")) # the file name of count data GetAssayData(d, "data") # SC_GDSMatrix GetAssayData(d, "scale.data") # SC_GDSMatrix scGetFiles(d) # the GDS file used in the Seurat object ``` ## See Also * [SCArray](http://www.bioconductor.org/packages/SCArray): Large-scale single-cell RNA-seq data manipulation with GDS files * [Seurat](https://cran.r-project.org/package=Seurat): A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data.