From the Cell Ranger Arc manual:

Cell Ranger ARC is a set of analysis pipelines that process Chromium Single Cell Multiome ATAC + Gene Expression sequencing data to generate a variety of analyses pertaining to gene expression, chromatin accessibility and their linkage. Furthermore, since the ATAC and gene expression measurements are on the very same cell, we are able to perform analyses that link chromatin accessibility and gene expression.

• cellranger-arc mkfastq demultiplexes raw base call (BCL) files generated by Illumina sequencers into FASTQ files. It is a wrapper around Illumina's bcl2fastq, with additional useful features that are specific to 10x libraries and a simplified sample sheet format. The same command can be used to demultiplex both ATAC and GEX flow cells.
• cellranger-arc count takes FASTQ files from cellranger-arc mkfastq and performs alignment, filtering, barcode counting, peak calling and counting of both ATAC and GEX molecules. Furthermore, it uses the Chromium cellular barcodes to generate feature-barcode matrices, perform dimensionality reduction, determine clusters, perform differential analysis on clusters and identify linkages between peaks and genes. The count pipeline can take input from multiple sequencing runs on the same GEM well.

These pipelines combine Chromium-specific algorithms with the widely used RNA-seq aligner STAR. Output is delivered in standard BAM, MEX, CSV, HDF5, and HTML formats that are augmented with cellular information and a .cloupe file for use with the Loupe browser.

• Home page + Manual

• Module Name: cellranger-arc (see the modules page for more information)
• cellranger can operate in local mode or cluster mode. In both cases, the local part of the job will use multiple CPUs. Users have to specify the number of allocated CPUs and amount of memory with --localcores=# --localmem=# to cellranger.
• cellranger may attempt to start more processes or open more files than the default limits on our compute nodes allow. If you encounter errors or strange results, you may have to raise these limits. See below for more deails.
• Reference data can be found in $CELLRANGER_ARC_REF
• Test data can be found in $CELLRANGER_ARC_TEST_DATA

Allocate an interactive session and run the program. Sample session:

Copy the bcl format test data and run the demux pipeline

[user@biowulf]$ sinteractive --cpus-per-task=6 --mem=35g
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144 ~]$ module load cellranger-arc
[user@cn3144 ~]$ cp ${CELLRANGER_ARC_TEST_DATA:-none}/* .
[user@cn3144 ~]$ tar -xzf cellranger-arc-tiny-bcl-atac-1.0.0.tar.gz
[user@cn3144 ~]$ tar -xzf cellranger-arc-tiny-bcl-gex-1.0.0.tar.gz
[user@cn3144 ~]$ # demultiplex the ATAC flowcell
[user@cn3144 ~]$ cellranger-arc mkfastq --id=tiny-bcl-atac \
                     --csv=cellranger-arc-tiny-bcl-atac-simple-1.0.0.csv \
                     --run=cellranger-arc-tiny-bcl-atac-1.0.0 \
                     --localcores=$SLURM_CPUS_PER_TASK \
                     --localmem=34
cellranger-arc mkfastq (cellranger-arc-1.0.0)
Copyright (c) 2020 10x Genomics, Inc.  All rights reserved.
-------------------------------------------------------------------------------

Martian Runtime - v4.0.1
Serving UI at http://cn1038:38947?auth=OKNOP2vgDnOXFmUoe1dK2y4rCKFuNCkYs16KtaTMqfw

Running preflight checks (please wait)...
Checking run folder...
Checking RunInfo.xml...
...
Pipestance completed successfully!

2020-10-06 20:30:54 Shutting down.
Saving pipestance info to "tiny-bcl-atac/tiny-bcl-atac.mri.tgz"

[user@cn3144 ~]$ # demultiplex the GEX flowcell
[user@cn3144 ~]$ cellranger-arc mkfastq --id=tiny-bcl-gex \
                     --csv=cellranger-arc-tiny-bcl-gex-simple-1.0.0.csv \
                     --run=cellranger-arc-tiny-bcl-gex-1.0.0 \
                     --localcores=$SLURM_CPUS_PER_TASK \
                     --localmem=34
...

Note that it is necessary to specify --localcores and --localmem.

Cellranger Arc may start an unreasonable number of processes or open too many files. If you encounter errors that include

...
 self.pid = os.fork()
OSError: [Errno 11] Resource temporarily unavailable 

or see unexpected results despite specifying --localcores and --localmem, you may have to raise the limit on the number of processes and/or open files allowed in your batch script:

[user@cn3144 ~]$ ulimit -u 10240 -n 16384

If running in slurm mode, it may be necessary to add --jobinterval=3000 if encountering errors mentioning empty batch scripts.

Generate counts per gene per cell

[user@cn3144 ~]$ cat > gex_atac.csv <<__EOF__
fastqs,sample,library_type
tiny-bcl-gex/outs/fastq_path/,test_sample_gex,Gene Expression
tiny-bcl-atac/outs/fastq_path/,test_sample_atac,Chromatin Accessibility
__EOF__
[user@cn3144 ~]$ cellranger-arc count \
                      --id=sample123 \
                      --reference=$CELLRANGER_ARC_REF/refdata-cellranger-arc-GRCh38-2020-A \
                      --libraries=gex_atac.csv \
                      --localcores=$SLURM_CPUS_PER_TASK \
                      --localmem=32
### note: example data currently fails at this step. waiting for reply from 10x support

The same job could also be run in cluster mode where pipeline tasks are submitted as batch jobs. This can be done by setting jobmode to slurm and limiting the max. number of concurrent jobs:

[user@cn3144 ~]$ cellranger-arc count \
                      --jobmode=slurm --maxjobs=10 \
                      --id=sample123 \
                      --reference=$CELLRANGER_ARC_REF/refdata-cellranger-arc-GRCh38-2020-A \
                      --libraries=gex_atac.csv \
                      --localcores=$SLURM_CPUS_PER_TASK \
                      --localmem=32

Don't forget to close the interactive session when done

[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$

Create a batch input file (e.g. cellranger.sh), which uses the input file 'cellranger.in'. For example:

#! /bin/bash
module load cellranger || exit 1
## uncomment the following line if encountering 'resource unavailable' errors
## despite using --localcores and --localmem
# ulimit -u 4096
cellranger-arc mkfastq --id=tiny-bcl-atac \
     --csv=cellranger-arc-tiny-bcl-atac-simple-1.0.0.csv \
     --run=cellranger-arc-tiny-bcl-atac-1.0.0 \
     --localcores=$SLURM_CPUS_PER_TASK \
     --localmem=34
cellranger-arc mkfastq --id=tiny-bcl-gex \
     --csv=cellranger-arc-tiny-bcl-gex-simple-1.0.0.csv \
     --run=cellranger-arc-tiny-bcl-gex-1.0.0 \
     --localcores=$SLURM_CPUS_PER_TASK \
     --localmem=34

cat > gex_atac.csv <<__EOF__
fastqs,sample,library_type
tiny-bcl-gex/outs/fastq_path/,test_sample_gex,Gene Expression
tiny-bcl-atac/outs/fastq_path/,test_sample_atac,Chromatin Accessibility
__EOF__
cellranger-arc count \
      --id=sample123 \
      --reference=$CELLRANGER_ARC_REF/refdata-cellranger-arc-GRCh38-2020-A \
      --libraries=gex_atac.csv \
      --localcores=6 \
      --localmem=32

Again, please remember to include --localcoes and --localmem.

Submit this job using the Slurm sbatch command.

sbatch --cpus-per-task=12 --mem=35g cellranger.sh

Create a swarmfile (e.g. cellranger.swarm). For example:

cellranger-arc mkfastq --run=./run1 --localcores=$SLURM_CPUS_PER_TASK --localmem=34
cellranger-arc mkfastq --run=./run2 --localcores=$SLURM_CPUS_PER_TASK --localmem=34
cellranger-arc mkfastq --run=./run3 --localcores=$SLURM_CPUS_PER_TASK --localmem=34

Submit this job using the swarm command.

swarm -f cellranger.swarm -g 35 -t 12 --module cellranger