Single cell gene expression data analysis on Cluster (10X Genomics, Cell Ranger)

7 minute read

Published:

Updated on May 03, 2021

Cell Ranger can be run in cluster mode, using job schedulers like Sun Grid Engine (or simply SGE) or Load Sharing Facility (or simply LSF) as queuing system allows highly parallelizable jobs.

There are 4 steps to analyze Chromium Single Cell data1.

Step 1: cellranger mkfastq demultiplexes raw base call (BCL) files generated by Illumina sequencers into FASTQ files.

Step 2: cellranger count takes FASTQ files from cellranger mkfastq and performs alignment, filtering, barcode counting, and unique molecular identifier (UMI) counting.

When doing large studies involving multiple GEM wells, first run cellranger count on FASTQ data from each of the GEM wells individually, and then pool the results using cellranger aggr, as described here.

Step 3: cellranger aggr aggregates outputs from multiple runs of cellranger count.

Step 4: Use R package Seurat2 for downstream analysis.

Running pipelines on cluster requires the following:

1. Download and uncompress cellranger-6.0.11 at your $HOME directory and add PATH in ~/.bashrc.

2. Update job config file (external/martian/jobmanagers/config.json) for threads and memory. For example

"threads_per_job": 4,
"memGB_per_job": 32,

3. Update template file sge.template (external/martian/jobmanagers/sge.template).

#!/bin/bash
#$ -N __MRO_JOB_NAME__
#$ -V
#$ -pe smp __MRO_THREADS__
#$ -q b.q
#$ -cwd
#$ -l mem_free=__MRO_MEM_GB__G
#$ -o __MRO_STDOUT__
#$ -e __MRO_STDERR__
#$ -m abe
#$ -M <email>
#$ -S /bin/bash
 
cd __MRO_JOB_WORKDIR__
source $HOME/cellranger-6.0.1/sourceme.bash 

For clusters whose job managers do not support memory requests, it is possible to request memory in the form of cores via the --mempercore command-line option. This option scales up the number of threads requested via the __MRO_THREADS__ variable according to how much memory a stage requires. see more at Cluster Mode

4. Download single cell gene expression and reference genome datasets from 10XGenomics.

5. Create sge.sh file

for Single Cell 3′ gene expression

Output files will appear in the out/ subdirectory within this pipeline output directory. For pipeline output directory, the --id argument is used i.e 10XGTX_v3.

#!/bin/bash

cd $HOME/10xgenomics/out  

FASTQS="$HOME/pbmc_10k_v3_fastqs"`  
TR="$HOME/refdata-cellranger-GRCh38-3.0.0

cellranger count --disable-ui \`  
  --id=10XGTX_v3 \`  
  --transcriptome=${TR} \`  
  --fastqs=${FASTQS} \`  
  --sample=pbmc_10k_v3 \`  
  --expect-cells=10000 \`  
  --jobmode=sge \`  
  --mempercore=8 \`  
  --jobinterval=5000 \`  
  --maxjobs=3`  

Execute a command in screen and, detach and reconnect:

Start a screen as screen -S some_name.
Run the above script as sh sge.sh
To detach the screen session from the terminal use control + a followed by d
To reconnect to the screen: screen -R some_name

for Single Cell 5′ gene expression

Use either --force-cells or --expect-cells

#!/bin/bash  

cd $HOME/10xgenomics/out  

TR="$HOME/refdata-cellranger-GRCh38-3.0.0
FASTQS="$HOME/vdj_v1_hs_nsclc_5gex_fastqs"

cellranger count \  
  --id=10XGTX_v5 \  
  --fastqs=${FASTQS} \  
  --transcriptome=${TR} \  
  --sample=vdj_v1_hs_nsclc_5gex \  
  --force-cells=7802 \  
  --jobmode=sge \  
  --mempercore=8 \  
  --maxjobs=3 \  
  --jobinterval=2000  

Execute a command in screen and, detach and reconnect:

Start a screen as screen -S some_name.
Run the above script as sh sge.sh
To detach the screen session from the terminal use control + a followed by d
To reconnect to the screen: screen -R some_name

for Feature Barcode Analysis

Tested on Single Cell 5′ gene expression and cell surface protein (Feature Barcoding/Antibody Capture Assay) data.

For more information, please visit the Single Cell Gene Expression with Feature Barcoding page (Single Cell 3’) or the Single Cell Immune Profiling with Feature Barcoding page (Single Cell 5’).

Currently available Feature Barcode kits for Single Cell Gene Expression Feature Barcode Technology

10x SolutionGene ExpressionCell Surface ProteinCRISPR Screening
Single Cell Gene Expression v2--
Single Cell Gene Expression v3
Single Cell Gene Expression v3.1
Single Cell Gene Expression v3.1 (Dual Index)

Currently available Feature Barcoding kits for Single Cell Immune Profiling Feature Barcoding Technology

10x SolutionTCR/IgGene ExpressionCell Surface MarkerTCR-Antigen Specificity
Single Cell Immune Profiling--
Single Cell Immune Profiling with Feature Barcoding technology

To enable Feature Barcode analysis, cellranger count needs two inputs:

First you need a csv file declaring input library data sources; one for the normal single-cell gene expression reads, and one for the Feature Barcode reads (the FASTQ file directory and library type for each input dataset).

LIBRARY=$HOME/vdj_v1_hs_pbmc2_5gex_protein_fastqs/vdj_v1_hs_pbmc2_5gex_protein_library.csv

fastqssamplelibrary_type
/path/to/antibody_fastqsvdj_v1_hs_pbmc2_antibodyAntibody Capture
/path/to/gene_expression_fastqsvdj_v1_hs_pbmc2_5gexGene Expression

Second, you need Feature reference csv file, declaring feature-barcode constructs and associated barcodes. The pattern will be used to extract the Feature Barcode sequence from the read sequence.

FEATURE_REF=$HOME/vdj_v1_hs_pbmc2_5gex_protein_fastqs/vdj_v1_hs_pbmc2_5gex_protein_feature_ref.csv

idnamereadpatternsequencefeature_type
CD3CD3_TotalSeqCR25PNNNNNNNNNN(BC)NNNNNNNNNCTCATTGTAACTCCTAntibody Capture
CD19CD19_TotalSeqCR25PNNNNNNNNNN(BC)NNNNNNNNNCTGGGCAATTACTCGAntibody Capture
CD45RACD45RA_TotalSeqCR25PNNNNNNNNNN(BC)NNNNNNNNNTCAATCCTTCCGCTTAntibody Capture

Feature and Library Types - When inputting Feature Barcode data to Cell Ranger via the Libraries CSV File, you must declare the library_type of each library. Examples include Antibody Capture, CRISPR Guide Capture or Custom. If your assay scheme creates a library containing multiple library_types, for example if you’re using CRISPR Guide Capture and Antibody Capture features, you will need to run Cell Ranger multiple times, passing different library_type values in the Libraries CSV File. If Targeted Gene Expression data is analyzed in conjunction with CRISPR-based Feature Barcode data, there are additional requirements imposed for the Feature Reference CSV file.

TotalSeq™ Reagents for Single-Cell Proteogenomics

TotalSeq™-B is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 3’ v3 assay.
TotalSeq™-C is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 5’ assay.
TotalSeq™-A is a line of antibody-oligonucleotide conjugates supplied by BioLegend that are compatible with the Single Cell 3’ v2 and Single Cell 3’ v3 kits. Although TotalSeq™-A can be used with the CITE-Seq assay, CITE-Seq is not a 10x supported assay.

CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) allows simultaneous analysis of transcriptome and cell surface protein information at the level of a single cell.3

“The pipeline first extracts and corrects the cell barcode and UMI from the feature library using the same methods as gene expression read processing. It then matches the Feature Barcode read against the list of features declared in the above Feature Barcode Reference. The counts for each feature are available in the feature-barcode matrix output files.”

#!/bin/bash  

cd $HOME/10xgenomics/out

TR="$HOME/refdata-cellranger-GRCh38-3.0.0

LIBRARY=$HOME/vdj_v1_hs_pbmc2_5gex_protein_fastqs/vdj_v1_hs_pbmc2_5gex_protein_library.csv  
FEATURE_REF=$HOME/vdj_v1_hs_pbmc2_5gex_protein_fastqs/vdj_v1_hs_pbmc2_5gex_protein_feature_ref.csv  

cellranger count \  
 --libraries=${LIBRARY} \  
 --feature-ref=${FEATURE_REF} \  
 --id=PBMC_5GEX \  
 --transcriptome=${TR} \  
 --expect-cells=9000 \  
 --jobmode=sge \  
 --mempercore=8 \  
 --maxjobs=3 \  
 --jobinterval=5000  

Execute a command in screen and, detach and reconnect:

Start a screen as screen -S some_name.
Run the above script as sh sge.sh.
To detach the screen session from the terminal use control + a followed by d
To reconnect to the screen: screen -R some_name

6. Monitor work progress through a web browser

Open _log file present in output folder PBMC_5GEX

If you see serving UI as http://cluster.university.edu:3600?auth=rlSdT_QLzQ9O7fxEo-INTj1nQManinD21RzTAzkDVJ8, then type the following from your laptop

ssh -NT -L 9000:cluster.university.edu:3600 user@cluster.university.edu

user@cluster.university.edu's password:

Then access the UI using the following URL in your web browser http://localhost:9000/

7. Single Cell Integration in Seurat

Seurat is an R package designed for QC, analysis, and exploration of single cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. Seurat starts by reading cellranger data (barcodes.tsv.gz, features.tsv.gz and matrix.mtx.gz)

pbmc.data <- Read10X(data.dir = "~/PBMC_5GEX/outs/filtered_feature_bc_matrix/")


  1. 10XGenomics  2

  2. Seurat 

  3. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).