R package installation

Objectives

• Learn how to run R efficiently on Tufts HPC using both graphical and command-line interfaces.

• Understand R package management, including installation and configuration on the HPC environment.

• Develop skills to run R scripts in batch mode, submit jobs to the queue, and manage multiple jobs effectively.

How to run R on Tufts HPC

1. Open OnDemand Rstudio app

1.1 Log in to Open OnDemand with your UTLN

https://ondemand.pax.tufts.edu/

1.2 You will see RStudio Pax under Interactive Apps and other topic-specific Rstudio apps under Bioinformatics Apps

1.3 Fill in the parameters according to your needs. You can start with default value.

1.4 Launch the job.

2. Command line interface

2.1 Make sure you are on the compute mode

srun -p interactive -n 1 --time=02:00:00 --mem 4g --pty bash

2.2 Load R modules

module load R/4.4.0

2.3 Launch R interactive session

R

R package installation

Pre-installed R packages

To provide convenience to users, we pre-installed many commonly used R packages used in bioinformatics analysis. Before you try to install packages, load the module for R, start R and check if the package is already installed.

library(packageName)

Get the list of installed R packages

installed.packages()

Install R packages

install.packages("packageName")

Rstudio

.libPaths()

This shows the folder where the packages are installed.

> .libPaths()
[1] "/cluster/home/xli37/R/x86_64-pc-linux-gnu-library/4.4"

Since we have limited storage under home directory. We suggest to change the libpath to folder under lab storage or research projects.

.libPaths("your_path_to_install_packages")

R modules

r base

 R/4.0.0 
 R/4.1.1 
 R/4.3.0 
 R/4.4.0

r-bioinformatics

$ module av r-bioinformatics

------- /cluster/tufts/biocontainers/modules ---------------
   r-bioinformatics/4.3.2    r-bioinformatics/4.4.0 (D)
  Where:
   D:  Default Module

r-scrnaseq

$ module av r-scrnaseq

------- /cluster/tufts/biocontainers/modules -----------------------------
   r-scrnaseq/4.2.3    r-scrnaseq/4.3.1    r-scrnaseq/4.3.2    r-scrnaseq/4.4.0 (D)

  Where:
   D:  Default Module

Bioconductor

Bioconductor is both an open source project and repository for R packages related to the analysis of biological data, primarily bioinformatics and computational biology, and as such it is a great place to search for -omics packages and pipelines.

Do not run the belew code to install bioconductor core packages

We have pre-installed bioconductor core packages for users. You do not need to run the below command to re-install it.

if (!require("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
BiocManager::install(version = "3.xx")

Installation with BiocManager

To install specific bioconductor packages, use BiocManager to install.

Ex: Let's install adverSCarial

BiocManager::install("adverSCarial")

Installation with devtools

devtools has been pre-installed in our R modules. You can load it to install packages require it.

library(devtools)

Installing Packages from GitHub

Ex: Install an R package STew used to jointly characterize the gene expression variation and spatial information.

devtools::install_github("fanzhanglab/STew")

Notice that for many R package, especially those process scRNA-Seq or spatial transcriptomics data, it often require dependencies or version-specific packages.