10x Genomics
Chromium Single Cell Immune Profiling

Cell Ranger6.1, printed on 11/21/2024

Analyzing V(D)J, Gene Expression & Feature Barcode with cellranger multi

In this section, you will learn:

• What is multi?
• When to use multi?
• Why use multi?
• Arguments and config for multi
• Running multi
• Successful multi run
• Example multi config CSVs
• Additional features in multi
• Features absent in multi
• Next steps

What is 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 (GEX) data. The cellranger multi pipeline analyzes these multiple library types together, enabling more consistent cell calling between the V(D)J and GEX data.

cellranger multi takes FASTQ files from cellranger mkfastq or bcl2fastq for any combination of 5' Gene Expression, Feature Barcode (cell surface protein or antigen), and V(D)J libraries from a single GEM well. It performs alignment, filtering, barcode counting, and UMI counting on the Gene Expression and/or Feature Barcode libraries. It also performs sequence assembly and paired clonotype calling on the V(D)J libraries. Additionally, the cell calls provided by the gene expression data are used to improve the cell calls from the V(D)J library. cellranger multi is the recommended pipeline for processing FASTQ files generated using the 5' the Immune Profiling Solution with Feature Barcode technology.

When to use multi?

Pipeline recommendation depends on the combination of libraries being analyzed. This table summarizes all library combinations and corresponding pipeline recommendations:

Why use multi?

The cellranger multi pipeline improves cell calls in the V(D)J dataset by discarding any cells that were not also called in the corresponding 5' GEX dataset. By assigning cells that are called in the V(D)J results but not in the 5' GEX results as background GEMs in the V(D)J data, the cellranger multi pipeline mitigates any overcalling issues that may arise in BCR and TCR data. This improved cell calling is only possible when both 5' GEX and V(D)J libraries were sequenced from the same sample.

As shown in the image below, final V(D)J cell calls (green intersection area) exclude cells that were only called by the vdj pipeline (yellow region).

The 5' GEX cell calls are not affected by the cellranger multi pipeline. 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 in the gene expression library 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 cellranger multi pipeline is run with both 5' GEX and V(D)J data, then barcodes which are not called as cells in the 5' GEX data will be deleted from the V(D)J cell set.

Arguments and config for multi

The cellranger multi pipeline takes FASTQ files as input. If you do not have FASTQ files for your V(D)J, single cell gene expression and/or Feature Barcode libraries, follow the instructions for running cellranger mkfastq to generate FASTQ files.

To simultaneously generate single cell feature counts, V(D)J sequences and annotations for a single library, run cellranger multi with the following arguments:

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 customizable template for a multi config CSV can be downloaded here, and example multi config CSVs can be downloaded from public datasets. Example formats for two product configurations are below.

Running multi

After determining the input arguments, run cellranger multi. Remember to replace the bits of code in red with your sample id and csv file path:

 mkdir /home/jdoe/runs
 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 - v4.0.6
 
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. If you wish to start the run over, delete the output folder (sample345/ in this example) and rerun the pipeline.

Successful multi run

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"

To learn more about the output files generated, refer to the Outputs for multi section under Understanding Outputs.

Example multi config CSVs

Here are the example multi config CSVs for two library combinations. Make sure to replace /path/to with the actual full path to your data, and edit any text in red according to the experiment's sample/library/file names.

Libraries	Multi config CSV
See example dataset	[vdj] reference,/path/to/vdj_reference [libraries] fastq_id,fastqs,lanes,feature_types,subsample_rate VDJ_B_fastqs_id,path/to/vdj_B_fastqs,1|2,vdj-b,
See example dataset Getting Started Tutorial	[gene-expression] reference,/path/to/transcriptome expect-cells,enter expected number of recovered cells include-introns,true [vdj] reference,/path/to/vdj_reference [libraries] fastq_id,fastqs,lanes,feature_types,subsample_rate GEX_fastqs_id,path/to/GEX_fastqs,1|2,gene expression, VDJ_B_fastqs_id,path/to/vdj_B_fastqs,1|2,vdj-b,
See example dataset	[gene-expression] reference,/path/to/transcriptome expect-cells,enter expected number of recovered cells include-introns,true [vdj] reference,/path/to/vdj_reference [feature] reference,/path/to/feature_ref.csv [libraries] fastq_id,fastqs,lanes,feature_types,subsample_rate GEX_fastqs_id,path/to/GEX_fastqs,1|2,gene expression, VDJ_B_fastqs_id,path/to/vdj_B_fastqs,1|2,vdj-b, VDJ_T_fastqs_id,path/to/vdj_T_fastqs,1|2,vdj-t, FB_fastqs_id,path/to/FB_fastqs,1|2,antibody capture,

Additional features in 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.

Features absent in multi

The option to run denovo without V(D)J reference (--denovo) is not supported in cellranger multi. This option is available in cellranger vdj

Next steps

Next, you may wish to:

• Understand multi outputs.
• Run cellranger aggr to aggregate the outputs from multiple runs of cellranger multi and perform analysis on the combined data.
• Explore Loupe and Loupe VDJ browser to visualize your data.