In zhengxwen/SCArray: Large-scale single-cell RNA-seq data manipulation with GDS files

Introduction

The SCArray package provides large-scale single-cell RNA-seq data manipulation using Genomic Data Structure (GDS) files. It combines dense/sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and manipulation using the R programming language. As shown in the figure, SCArray provides a SingleCellExperiment object for downstream data analyses. GDS is an alternative to HDF5. Unlike HDF5, GDS supports the direct storage of a sparse matrix without converting it to multiple vectors.

Installation

• Requires R (>= v3.5.0), gdsfmt (>= v1.24.0)

• Bioconductor repository

To install this package, start R and enter: ```{R, eval=FALSE} if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("SCArray")

## Format conversion

### Conversion from SingleCellExperiment

The SCArray package can convert a single-cell experiment object
(SingleCellExperiment) to a GDS file using the function `scConvGDS()`.
For example,

```r
suppressPackageStartupMessages(library(SCArray))
suppressPackageStartupMessages(library(SingleCellExperiment))

# load a SingleCellExperiment object
fn <- system.file("extdata", "LaMannoBrainSub.rds", package="SCArray")
sce <- readRDS(fn)

# convert to a GDS file
scConvGDS(sce, "test.gds")

# list data structure in the GDS file
(f <- scOpen("test.gds")); scClose(f)

Conversion from a matrix

The input of scConvGDS() can be a dense or sparse matrix for count data:

library(Matrix)

cnt <- matrix(0, nrow=4, ncol=8)
set.seed(100); cnt[sample.int(length(cnt), 8)] <- rpois(8, 4)
(cnt <- as(cnt, "dgCMatrix"))

# convert to a GDS file
scConvGDS(cnt, "test.gds")

Single cell datasets

When a single-cell GDS file is available, users can use scExperiment() to load a SingleCellExperiment object from the GDS file. The assay data in the SingleCellExperiment object are DelayedMatrix objects to avoid the memory limit.

# a GDS file in the SCArray package
(fn <- system.file("extdata", "LaMannoBrainData.gds", package="SCArray"))
# load a SingleCellExperiment object from the file
sce <- scExperiment(fn)
sce

# it is a DelayedMatrix (the whole matrix is not loaded)
assays(sce)$counts

# column data
colData(sce)
# row data
rowData(sce)

Data Manipulation and Analysis

SCArray provides a SingleCellExperiment object for downstream data analyses. At first, we create a log count matrix logcnt from the count matrix. Note that logcnt is also a DelayedMatrix without actually generating the whole matrix.

cnt <- assays(sce)$counts
logcnt <- log2(cnt + 1)
assays(sce)$logcounts <- logcnt
logcnt

Mean for each column or row

The DelayedMatrixStats package provides functions operating on rows and columns of DelayedMatrix objects. For example, we can calculate the mean for each column or row of the log count matrix.

suppressPackageStartupMessages(library(DelayedMatrixStats))

col_mean <- DelayedMatrixStats::colMeans2(logcnt)
str(col_mean)
row_mean <- DelayedMatrixStats::rowMeans2(logcnt)
str(row_mean)

UMAP analysis

The scater package can perform the uniform manifold approximation and projection (UMAP) for the cell data, based on the data in a SingleCellExperiment object.

suppressPackageStartupMessages(library(scater))

# run umap analysis
sce <- runUMAP(sce)

plotReducedDim() plots cell-level reduced dimension results (UMAP) stored in the SingleCellExperiment object:

plotReducedDim(sce, dimred="UMAP")

Session Info

# print version information about R, the OS and attached or loaded packages
sessionInfo()

unlink("test.gds", force=TRUE)

zhengxwen/SCArray documentation built on Jan. 1, 2021, 1:54 p.m.