Updated on August 30, 2020
Running cellranger as cluster mode that uses Sun Grid Engine (SGE) as queuing system allows highly parallelizable jobs.
There are 4 steps to analyze Chromium Single Cell data1.
cellranger mkfastq demultiplexes raw base call (BCL) files generated by Illumina sequencers into FASTQ files.
cellranger count takes FASTQ files from cellranger mkfastq and performs alignment, filtering, barcode counting, and UMI counting. When doing large studies involving multiple GEM wells, run cellranger count on FASTQ data from each of the GEM wells individually, and then pool the results using cellranger aggr, as described here.
cellranger aggr aggregates outputs from multiple runs of cellranger count.
Step 4: Downstream/Secondary analysis using R package Seurat v3.02.
Running pipelines on cluster requires the following:
1. Download and uncompress
cellranger-4.0.01 at your
$HOME directory and add PATH in
2. Update job config file (
cellranger-4.0.0/martian-cs/vx.x.x/jobmanagers/config.json) for threads and memory. For example
3. Update template file (
#$ -pe smp __MRO_THREADS__
##$ -l mem_free=__MRO_MEM_GB__G (comment this line if your cluster do not support it!)
#$ -q b.q
#$ -S /bin/bash
#$ -m abe
#$ -M <e-mail>
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.
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.
cellranger count --disable-ui \
for Single Cell 5′ gene expression
use either –force-cells or –expect-cells
cellranger count \
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 Solution||Gene Expression||Cell Surface Protein||CRISPR Screening|
|Single Cell Gene Expression v2||✓||-||-|
|Single Cell Gene Expression v3||✓||✓||✓|
|Single Cell Gene Expression v3.1||✓||✓||✓|
Currently available Feature Barcoding kits for Single Cell Immune Profiling Feature Barcoding Technology
|10x Solution||TCR/Ig||Gene Expression||Cell Surface Marker||TCR-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).
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 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™-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.”
cellranger count \
6. Execute a command in screen and, detach and reconnect
screen command to get in/out of the system while keeping the processes running.
screen -S screen_name
If you want to exit the terminal without killing the running process, simply press
To reconnect to the screen:
screen -R screen_name
7. Monitor work progress through a web browser
_log file present in output folder
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 email@example.com
Then access the UI using the following URL in your web browser
8. Single Cell Integration in Seurat v3.1.5
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/")