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10x Genomics
Chromium Single Cell Immune Profiling

Analyzing V(D)J and Gene Expression / Feature Barcode with cellranger multi

The 5' Chromium Single Cell Immune Profiling Solution with Feature Barcode technology enables simultaneous profiling of V(D)J repertoire, cell surface protein, antigen specificity and gene expression data. The cellranger multi pipeline enables the analysis of these multiple library types together. The advantage of using the multi pipeline (as opposed to using cellranger vdj and cellranger count separately) is that it enables more consistent cell calling between the V(D)J and gene expression data. This involves the following steps:

  1. Run cellranger mkfastq on the Illumina BCL output folder to generate FASTQ files.

  2. Run cellranger multi on FASTQ files produced by cellranger mkfastq.

Run cellranger mkfastq

First, follow the instructions on running cellranger mkfastq to generate FASTQ files. For example, if the flowcell serial number was HAWT7ADXX, then cellranger mkfastq will output FASTQ files in HAWT7ADXX/outs/fastq_path.

Run cellranger multi

To simultaneously generate single-cell feature counts, V(D)J sequences and annotations for a single library, run cellranger multi. This requires a config CSV, which is described below, and invoking cellranger multi with the following arguments:

ArgumentDescription
--idA unique run ID string: e.g. sample345 that is also the output folder name. Cannot be more than 64 characters.
--csvPath to config CSV file enumerating input libraries and analysis parameters.

The multi config CSV contains both the library definitions and experiment configuration variables. It is composed of up to four sections: [gene-expression], [feature], [vdj], and [libraries]. The [gene-expression], [feature], and [vdj] sections have at most two columns, and are responsible for configuring their respective portions of the experiment. The [libraries] section specifies where input FASTQ files may be found. A template for a multi config CSV can be downloaded here, and example multi config CSVs can be downloaded from 5.0.0 public datasets here.

Multi Config CSV
Section: [gene-expression]
FieldDescription
referencePath of folder containing 10x-compatible reference. Required for gene expression and Feature Barcode libraries.
target-panelPath to a target panel CSV file or name of a 10x Genomics fixed gene panel (pathway, pan-cancer, immunology, neuroscience). Optional.
no-target-umi-filterDisable targeted UMI filtering stage. Optional. Default: false.
r1-lengthHard trim the input Read 1 of gene expression libraries to this length before analysis. Optional. Default: do not trim Read 1.
r2-lengthHard trim the input Read 2 of gene expression libraries to this length before analysis. Optional. Default: do not trim Read 2.
chemistryAssay configuration. NOTE: by default, the assay configuration is detected automatically, which is the recommended mode. Users usually will not need to specify a chemistry. Options are: 'auto' for autodetection, 'threeprime' for Single Cell 3', 'fiveprime' for Single Cell 5', 'SC3Pv1' or 'SC3Pv2' or 'SC3Pv3' for Single Cell 3' v1/v2/v3, 'SC5P-PE' or 'SC5P-R2' for Single Cell 5', paired-end/R2-only, 'SC-FB' for Single Cell Antibody-only 3' v2 or 5'. Default: auto.
expect-cellsExpected number of recovered cells. Optional. Default: 3000.
force-cellsForce pipeline to use this number of cells, bypassing cell detection. Optional. Default: detect cells using EmptyDrops.
include-intronsInclude intronic reads in count. Default: false
no-secondaryDisable secondary analysis, e.g. clustering. Optional. Default: false.
no-bamDo not generate a bam file. Default: false.
Section: [feature]
FieldDescription
referenceFeature reference CSV file, declaring Feature Barcode constructs and associated barcodes. Required for Feature Barcode libraries, otherwise optional.
r1-lengthHard trim the input Read 1 of Feature Barcode libraries to this length before analysis. Optional. Default: do not trim Read 1.
r2-lengthHard trim the input Read 2 of Feature Barcode libraries to this length before analysis. Optional. Default: do not trim Read 2.
Section: [vdj]
FieldDescription
referencePath of folder containing 10x-compatible VDJ reference. Required for VDJ Immune Profiling libraries.
inner-enrichment-primersIf inner enrichment primers other than those provided in the 10x kits are used, they need to be specified here as a text file with one primer per line. Optional.
r1-lengthHard trim the input Read 1 of V(D)J libraries to this length before analysis. Optional. Default: do not trim Read 1.
r2-lengthHard trim the input Read 2 of V(D)J libraries to this length before analysis. Optional. Default: do not trim Read 2.
Section: [libraries]
ColumnDescription
fastq_idRequired. The Illumina sample name to analyze. This will be as specified in the sample sheet supplied to mkfastq or bcl2fastq.
fastqsRequired. The folder containing the FASTQ files to be analyzed. Generally, this will be the fastq_path folder generated by cellranger mkfastq.
lanesThe lanes associated with this sample, separated by `|`. Defaults to using all lanes. Optional
feature_typesRequired. The underlying feature type of the library, which must be one of ‘Gene Expression’, ‘VDJ’, ‘VDJ-T’, ‘VDJ-B’, ‘Antibody Capture’, ‘CRISPR Guide Capture’.
subsample_rateThe rate at which reads from the provided FASTQ files are sampled. Must be in (0-1]. Optional.

After determining these input arguments, run cellranger:

$ cd /home/jdoe/runs
$ cellranger multi --id=sample345 --csv=/home/jdoe/sample345.csv

Following a series of checks to validate input arguments, cellranger multi pipeline stages will begin to run:

Martian Runtime - v4.0.2
 
Running preflight checks (please wait)...
...

By default, cellranger will use all of the cores available on your system to execute pipeline stages. You can specify a different number of cores to use with the --localcores option; for example, --localcores=16 will limit cellranger to using up to sixteen cores at once. Similarly, --localmem will restrict the amount of memory (in GB) used by cellranger.

The pipeline will create a new folder named with the run ID you specified using the --id argument (e.g. /home/jdoe/runs/sample345) for its output. If this folder already exists, cellranger will assume it is an existing pipestance and attempt to resume running it.

Output Files

A successful cellranger multi run should conclude with a message similar to this:

Waiting 6 seconds for UI to do final refresh.
Pipestance completed successfully!
 
yyyy-mm-dd hh:mm:ss Shutting down.
Saving pipestance info to "tiny/tiny.mri.tgz"

The output of the pipeline will be contained in a folder named with the run ID you specified (e.g. sample345). The subfolder named outs will contain the main pipeline outputs:

File NameDescription
web_summary.htmlRun summary metrics and charts in HTML format
config.csvThe input multi config CSV
countThe results of any gene-expression and feature barcode analysis, similar to cellranger count, described here
vdj_bThe results of any V(D)J Immune Profiling analysis for any B cells, similar to cellranger vdj, described here
vdj_tThe results of any V(D)J Immune Profiling analysis for any T cells, similar to cellranger vdj, described here

Once cellranger multi has successfully completed, you can browse the resulting summary HTML file in any supported web browser, or refer to the count and vdj sections to explore the data by hand.

When to use the multi pipeline

VDJ5' GEX5' FBUse multi?
YesYesYesRecommended
YesYesNoRecommended
YesNoYesOptional. No effect on cell calling
YesNoNoOptional
NoNoYesOptional
NoYesNoOptional
NoYesYesOptional

The gene expression library is representative of the entire pool of poly-adenylated mRNA transcripts captured within each partition (droplet). The TCR or BCR transcripts are then selectively amplified to create the V(D)J library. Therefore, the gene expression library has more power to detect partitions containing cells compared to the V(D)J library. If the multi pipeline is run with both gene expression and VDJ data, then barcodes which are not called as cells by using the gene expression data will be deleted from the V(D)J cell set.

Features not in cellranger multi

The --denovo option available in cellranger vdj is not supported in cellranger multi.

Additional features in cellranger multi

The cellranger multi pipeline supports downsampling the reads by specifying a rate between 0 and 1 independently for each library. It also allows trimming the reads to a fixed length, which is not supported in the cellranger vdj pipeline.