In IMB-Computational-Genomics-Lab/ascend: ascend - Analysis of Single Cell Expression, Normalisation, and Differential expression

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Before you begin

System requirements

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.

Dependancy installation

ascend relies on packages found on CRAN and Bioconductor. Please install these packages before installing ascend.

1.3.1 Packages from CRAN

You can use the install.packages() to install the packages described in this section. The pcakages you require from this repository are as follows:

1. devtools: This package will be used to load the development version of ascend.
2. tidyverse: This is a series of R packages for data science and visualisation. This will install packages such as dplyr, ggplot2 and tidyr.
3. data.table: Please follow the instructions on this page for your operating system.

Packages from Bioconductor

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.

Setting up Bioconductor

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/scran package installation

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.

Differential expression packages

ascend provides wrappers for DESeq and DESeq2, so you may choose to add them to your installation.

Possible issues with the 'stringi' package

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

Installing 'ascend' via devtools

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)

Configuring BiocParallel

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.

Unix/Linux/MacOS (Single Machine)

library(BiocParallel)
ncores <- parallel::detectCores() - 1
register(MulticoreParam(workers = ncores, progressbar=TRUE), default = TRUE)

Windows (Single Machine - Quad-core system)

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)

IMB-Computational-Genomics-Lab/ascend documentation built on Aug. 29, 2019, 4:10 a.m.