To follow along, you must:
- Have basic UNIX command line experience
- Fulfill these system requirements
- Download and install the Cell Ranger software
- Choose a compute platform
- Have access to a UNIX command prompt
We will work with the 2k Transgenic HEL Mouse Splenocytes (BEAM-Ab) dataset.
Open up a terminal window. You may log in to a remote server or choose to perform the compute on your local machine. Refer to the System Requirements page for details.
In the working directory, create a new folder called beam-ab and cd into that folder:
mkdir beam-ab
cd beam-ab
Download the input FASTQ files:
curl -O https://cf.10xgenomics.com/samples/cell-vdj/7.1.0/2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex/2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_fastqs.tar
A file named 2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_fastqs.tar should appear in your directory when you list files with the ls -lt command.
Uncompress the FASTQs:
tar -xvf 2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_fastqs.tar
You should now see a folder called 2k_BEAM-Ab_Mouse_HEL_5pv2_fastqs
cd 2k_BEAM-Ab_Mouse_HEL_5pv2_fastqs
ls
The folder contains three subfolders with library-specific FASTQS files: antigen_capture, gex, and vdj.
Navigate back to the working directory:
cd ..
Double check you are in the correct directory by running the ls command; the working directory should have the FASTQs 2k_BEAM-Ab_Mouse_HEL_5pv2_fastq folder.
Download the Feature Reference CSV available for this example dataset.
curl -O https://cf.10xgenomics.com/samples/cell-vdj/7.1.0/2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex/2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_count_feature_reference.csv
To view the contents of the Feature Reference CSV, open it in your text editor of choice (e.g., nano)
nano 2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_count_feature_reference.csv
The contents should look like this:
id,name,read,pattern,sequence,feature_type
SARS-TRI-S_WT,SARS-TRI-S_WT,R2,^(BC),CGATGCCGGACGATC,Antigen Capture
Anti-Hen_Egg_Lysozyme,Anti-Hen_Egg_Lysozyme,R2,^(BC),CCGTCTCACCGATAT,Antigen Capture
gp120,gp120,R2,^(BC),GATTGGCTACTCAAT,Antigen Capture
H5N1,H5N1,R2,^(BC),CGGCTCACCGCGTCT,Antigen Capture
negative_control,negative_control,R2,^(BC),CTATCTACCGGCTCG,Antigen Capture
Since this is a BEAM-Ab (BCR Antigen Capture) dataset, the Feature Reference CSV does NOT contain the additional mhc_allele column. The BEAM-T tutorial tutorial guides you through analyzing a TCR Antigen Capture dataset.
You do not need to change the Feature Reference CSV for this tutorial. Remember to customize it when working with your own data. Learn more about the Feature Reference CSV.
Download the pre-built mouse reference transcriptome to the working directory (beam-ab) and uncompress it:
curl -O https://cf.10xgenomics.com/supp/cell-vdj/refdata-gex-mm10-2020-A.tar.gz
tar -xvf refdata-gex-mm10-2020-A.tar.gz
Download the custom built mouse V(D)J reference in the working directory and uncompress it:
curl -O https://cf.10xgenomics.com/samples/cell-vdj/7.1.0/2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex/2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_vdj_reference.tar.gz
tar -xvf 2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_vdj_reference.tar.gz
In your working directory, create a new CSV file called 2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_config.csv using your text editor of choice. For example, you can create a file with nano using this command:
nano 2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_config.csv
Copy and paste this text into the newly created file and customize the /path/to/... part of file paths:
[gene-expression]
ref,/path/to/references/refdata-gex-mm10-2020-A
[feature]
ref,/path/to/feature_references/2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_count_feature_reference.csv
[vdj]
ref,/path/to/references/vdj_reference
[libraries]
fastq_id,fastqs,lanes,feature_types
beamab_mouse_hel_ag,/path/to/fastqs/2k_BEAM-Ab_Mouse_HEL_5pv2_fastqs/antigen_capture,1|2|3|4,Antigen Capture
beamab_mouse_hel_vdj,/path/to/fastqs/2k_BEAM-Ab_Mouse_HEL_5pv2_fastqs/vdj,1|2|3|4,VDJ-B
beamab_mouse_hel_gex,/path/to/fastqs/2k_BEAM-Ab_Mouse_HEL_5pv2_fastqs/gex,1|2|3|4,Gene Expression
[antigen-specificity]
control_id,
negative_control
Use your text editor's save command to save the file. In nano, save by typing CTRL+X → y → ENTER.
A customizable multi config CSV template is available for download on the example dataset page, under the Input Files tab.
Once you have all the necessary files, make a new directory called runs/ in your beam-ab/ working directory:
mkdir runs/
cd runs/
You will run cellranger multi in the runs/ directory.
After downloading/creating the FASTQ files, Feature Reference CSV, reference transcriptome, and V(D)J reference, you are ready to run cellranger multi.
Print the usage statement to get a list of all the options:
cellranger multi --help
The output should look similar to:
user_prompt$ cellranger multi --help
cellranger-multi
Analyze multiplexed data or combined gene expression/immune profiling/feature
barcode data
USAGE:
cellranger multi [FLAGS] [OPTIONS] --id --csv
FLAGS:
--dry Do not execute the pipeline. Generate a pipeline
invocation (.mro) file and stop
--disable-ui Do not serve the web UI
--noexit Keep web UI running after pipestance completes or fails
--nopreflight Skip preflight checks
-h, --help Prints help information
OPTIONS:
--id A unique run id and output folder name [a-zA-Z0-
9_-]+
--description Sample description to embed in output files
[default: ]
--csv Path of CSV file enumerating input libraries and
analysis parameters
--jobmode Job manager to use. Valid options: local
(default), sge, lsf, slurm or path to a
.template file. Search for help on "Cluster
Mode" at support.10xgenomics.com for more
details on configuring the pipeline to use a
compute cluster [default: local]
--localcores Set max cores the pipeline may request at one
time. Only applies to local jobs
....
Options used in this tutorial
| Option | Description |
|---|---|
--id | The id argument must be a unique run ID. We will call this run HumanB_Cell_multi based on the sample type in the example dataset. |
--csv | Path to the multi config CSV file enumerating input libraries and analysis parameters. Your multi_config.csv file is in the working directory. When executing cellranger multi from the runs directory, the relative path should be: ../multi_config.csv |
From within the beam-ab/runs/ directory, run cellranger multi
/path/to/cellranger-7.1.0/cellranger multi --id=beam-ab-run --csv=../2k_BEAM-Ab_Mouse_HEL_5pv2_Multiplex_config.csv
The run begins similarly to this:
Martian Runtime - v4.0.10
2023-06-15 11:44:24 [jobmngr] WARNING: configured to use 334GB of local memory, but only 194.9GB is currently available.
Serving UI at http://bespin3.fuzzplex.com:34513?auth=-Sm5gsg6_G8FjcUX0_YD5J8SYoBODz4IWoVIK9ec0jg
Running preflight checks (please wait)...
2023-06-15 11:44:33 [runtime] (ready) ID.beam-ab-run.SC_MULTI_CS.PARSE_MULTI_CONFIG
2023-06-15 11:44:33 [runtime] (run:local) ID.beam-ab-run.SC_MULTI_CS.PARSE_MULTI_CONFIG.fork0.chnk0.main
2023-06-15 11:44:56 [runtime] (chunks_complete) ID.beam-ab-run.SC_MULTI_CS.PARSE_MULTI_CONFIG
2023-06-15 11:44:56 [runtime] (ready) ID.beam-ab-run.SC_MULTI_CS.FULL_COUNT_INPUTS.WRITE_GENE_INDEX
2023-06-15 11:44:56 [runtime] (run:local) ID.beam-ab-run.SC_MULTI_CS.FULL_COUNT_INPUTS.WRITE_GENE_INDEX.fork0.chnk0.main
....
When the output of the cellranger multi command says, “Pipestance completed successfully!”, the job is done:
web_summary: /jane.doe/ab/runs/beam-ab-run/outs/per_sample_outs/beam-ab/web_summary.html
metrics_summary: /jane.doe/beam-ab/runs/beam-ab-run/runs/beam-ab/outs/per_sample_outs/beam-ab/metrics_summary$
}
Waiting 6 seconds for UI to do final refresh.
Pipestance completed successfully!
A successful cellranger multi run produces a new directory called beam-ab-run (based on the --id flag specified during the run). The contents of the beam-ab-run/ directory:
.
├── beam-ab-run
│ ├── beam-ab.mri.tgz
│ ├── _cmdline
│ ├── _filelist
│ ├── _finalstate
│ ├── _invocation
│ ├── _jobmode
│ ├── _log
│ ├── _mrosource
│ ├── outs
│ ├── _perf
│ ├── _perf._truncated_
│ ├── SC_MULTI_CS
│ ├── _sitecheck
│ ├── _tags
│ ├── _timestamp
│ ├── _uuid
│ ├── _vdrkill
│ └── _versions
The outs/ directory contains all important output files generated by the cellranger multi pipeline:
── runs
└── beam-ab-run
└──outs
├── config.csv
├── multi
│ ├── count
│ │ ├── feature_reference.csv
│ │ ├── raw_cloupe.cloupe
│ ├── raw_feature_bc_matrix
│ │ ├── raw_feature_bc_matrix.h5
│ │ ├── raw_molecule_info.h5
│ │ ├── unassigned_alignments.bam
│ │ └── unassigned_alignments.bam.bai
│ └── vdj_b
│ ├── all_contig_annotations.bed
│ ├── all_contig_annotations.csv
│ ├── all_contig_annotations.json
│ ├── all_contig.bam
│ ├── all_contig.bam.bai
│ ├── all_contig.fasta
│ ├── all_contig.fasta.fai
│ └── all_contig.fastq
├── per_sample_outs
│ └── beam-ab
│ ├── antigen_analysis
│ ├── count
│ ├── metrics_summary.csv
│ ├── vdj_t
│ └── web_summary.html
└── vdj_reference
├── fasta
│ ├── donor_regions.fa
│ └── regions.fa
└── reference.json