Large-scale single-cell RNA-seq data analysis using GDS files and Seurat
====
[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.
## 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.
