knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Datasets produced by single cell RNA sequencing (scRNA-seq) experiments are
very large and can range from a few hundred to a million cells. The number of
cells affect the amount of computational resources required to process the
dataset – therefore, you need to determine if you have enough computational
power and time to complete the analysis. ascend
can comfortably analyse
datasets of up to 10,000 cells on a single machine with 8GB of RAM and a
quad-core CPU. Larger datasets should be run on a High Performance Cluster
(HPC).
We have tested this package on datasets ranging from 100 to 70,000 cells. Generally, increasing the number of CPUs will decrease the processing time of functions, while larger datasets require more RAM.
ascend
relies on packages found on CRAN and Bioconductor. Please install
these packages before installing ascend
.
You can use the install.packages() to install the packages described in this section. The pcakages you require from this repository are as follows:
ascend
.Bioconductor is a repository for R packages related to the analysis and comprehension of high-throughput genomic data. It uses a separate set of commands for the installation of packages.
Use the BiocManager
package to load the Bioconductor installer.
if (!requireNamespace("BiocManager")) install.packages("BiocManager") BiocManager::install()
You can then install the Bioconductor packages using install
.
bioconductor_packages <- c("Biobase", "BiocGenerics", "BiocParallel", "SingleCellExperiment", "GenomeInfoDb", "GenomeInfoDbData") BiocManager::install(bioconductor_packages)
scater and scran are scRNA-seq analysis toolboxes that provide more in-depth methods for QC and filtering. You may choose to install these packages if you wish to take advantage of the wrappers provided for these packages.
ascend
provides wrappers for DESeq
and DESeq2,
so you may choose to add them to your installation.
There may be issues for some users related to the R package "stringi". This package is a dependancy for some of the packages from Bioconductor. Try installing this package from this website
As ascend
is still under development, we will use devtools to install the
package.
# Load devtools package library(devtools) # Use devtools to install the package install_github("powellgenomicslab/ascend", build_vignettes = TRUE)
The package can then be loaded as normal.
# Load the package in R library(ascend)
This package makes extensive use of BiocParallel, enabling ascend
to make the most of your computer's hardware. As each system is different, BiocParallel needs to be configured by the user. Here are some example configurations.
library(BiocParallel) ncores <- parallel::detectCores() - 1 register(MulticoreParam(workers = ncores, progressbar=TRUE), default = TRUE)
The following commands allows Windows to parallelise functions via BiocParallel. Unlike multicore processing in *nix systems, Snow creates additional R sessions to export tasks to. This requires additional computational resources to run and manage the tasks.
We recomend you bypass this step if your machine has lower specs.
library(BiocParallel) workers <- 3 # Number of cores on your machine - 1 register(SnowParam(workers = workers, type = "SOCK", progressbar = TRUE), default = TRUE)
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