Installation

To use the scRNAbox pipeline, the folowing must be installed on your High-Performance Computing (HPC) system:


scrnabox.slurm installation

scrnabox.slurm is written in bash and can be used with any Slurm system. To download the latest version of scrnabox.slurm (v0.1.35) run the following command:

wget https://github.com/neurobioinfo/scrnabox/releases/download/v0.1.35/scrnabox.slurm.zip
unzip scrnabox.slurm.zip

For a description of the options for running scrnabox.slurm run the following command:

bash /pathway/to/scrnabox.slurm/launch_scrnabox.sh -h 

If the scrnabox.slurm has been installed properly, the above command should return the folllowing:

        mandatory arguments:
                -d  (--dir)  = Working directory (where all the outputs will be printed) (give full path)
                --steps  =  Specify what steps, e.g., 2 to run just step 2, 2-4, run steps 2 through 4)

        optional arguments:
                -h  (--help)  = See helps regarding the pipeline options. 
                --method  = Choose what scRNA method you want to use; use HTO  and SCRNA for for hashtag nad Standard scRNA, respectively. 
                --nFeature_RNA_L  = Lower threshold of number of unique RNA transcripts for each cell, it filters nFeature_RNA > nFeature_RNA_L.  
                --nFeature_RNA_U  = Upper threshold of number of unique RNA transcripts for each cell, it filters --nFeature_RNA_U.  
                --nCount_RNA_L  = Lower threshold for nCount_RNA, it filters nCount_RNA > nCount_RNA_L   
                --nCount_RNA_U  = Upper threshold for  nCount_RNA, it filters nCount_RNA < nCount_RNA_U  
                --mitochondria_percent_L  = Lower threshold for the amount of mitochondrial transcript, it is in percent, mitochondria_percent > mitochondria_percent_L. 
                --mitochondria_percent_U  = Upper threshold for the amount of mitochondrial transcript, it is in percent, mitochondria_percent < mitochondria_percent_U. 
                --log10GenesPerUMI_U  = Upper threshold for the log number of genes per UMI for each cell, it is in percent,log10GenesPerUMI=log10(nFeature_RNA)/log10(nCount_RNA). mitochondria_percent < log10GenesPerUMI_U. 
                --log10GenesPerUMI_L  = Lower threshold for the log number of genes per UMI for each cell, log10GenesPerUMI=log10(nFeature_RNA)/log10(nCount_RNA). mitochondria_percent > log10GenesPerUMI_L.  
                --msd  = you can get the hashtag labels by running the following code 
                --marker  = Find marker. 
                --sinfo  = Do you need sample info? 
                --fta  = FindTransferAnchors 
                --enrich  = Annotation 
                --dgelist  = creates a DGEListobject from a table of counts obtained from seurate objects. 
                --genotype  = Run the genotype contrast. 
                --celltype  = Run the Genotype-cell contrast. 
                --cont  = You can directly call the contrast to the pipeline.  
                --seulist                = You can directly call the list of seurat objects to the pipeline. 

CellRanger installation

For information regarding the installation of CellRanger, please visit the 10X Genomics documentation. If CellRanger is already installed on your HPC system, you may skip the CellRanger installation procedures.


R library preparation and R package installation

Users must first install R onto their HPC system:

# install R
module load r/4.2.1

Then, users should create a designated directory on their HPC system where the required R packages will be installed:

# make common R library
mkdir R_library
cd R_library

# open R
R 

# set common R library path
R_LIB_PATH="/pathway/to/R_library"
.libPaths(R_LIB_PATH)

# load packages
library(Seurat)
library(ggplot2)
library(dplyr)
library(foreach)
library(doParallel)
library(Matrix)
library(DoubletFinder)
library(cowplot)
library(clustree)
library(xlsx)
library(enrichR)
library(stringi)
library(limma)
library(tidyverse)
library(edgeR)
library(vctrs)
library(RColorBrewer)
library(fossil)
library(openxlsx)
library(stringr)
library(ggpubr)
devtools::install_github(“neurobioinfo/scrnabox/scrnaboxR”)

Upon completing the installation procedures, users can proceed with the scRNAbox pipeline using either the Standard scRNAseq Analysis Track or Cell Hashtag scRNAseq Analysis Track.