Cell Ranger7.0 (latest), printed on 05/21/2022
Cell Ranger's pipelines analyze sequencing data produced from Chromium Single Cell Gene Expression. It also processes data generated by using Feature Barcode technology and/or Single Cell Targeted Gene Expression.
|New in Cell Ranger v7.0: Intronic reads are counted by default for whole transcriptome gene expression data. For more information, see our recommendation on including introns for gene expression analysis page.|
The analysis involves the following steps:
Run cellranger mkfastq on the Illumina BCL output folder to generate FASTQ files.
Run cellranger count on each GEM well that was demultiplexed by cellranger mkfastq. For Targeted Gene Expression libraries, see Targeted Gene Expression Analysis for instructions on how to provide the target gene panel information. If you created a Feature Barcode library alongside the Gene Expression library, you will pass them both to cellranger count at this point. See Feature Barcode Analysis for details.
Optionally, run cellranger aggr to aggregate multiple GEM wells from a single experiment that were analyzed by cellranger count.
Optionally run cellranger reanalyze to re-run the secondary analysis on a library or aggregated set of libraries (i.e., PCA, t-SNE, and clustering) and be able to fine-tune parameters.
For the following example, assume that the Illumina BCL output is in a folder named
First, follow the instructions on running cellranger mkfastq to generate FASTQ files. For example, if the flow cell ID was
HAWT7ADXX, then cellranger mkfastq will output FASTQ files in
To generate single cell feature counts for a single library, run cellranger count with the following arguments. For a complete listing of the arguments accepted, see the Command Line Argument Reference below, or run cellranger count --help. Cell Ranger must not be used for Single Cell Multiome Analysis. For Single Cell Multiome ATAC + Gene Expression libraries, use Cell Ranger ARC.
Starting in Cell Ranger 7.0, the expected number of cells can either be auto-estimated or specified with |
|For help on which arguments to use to target a particular set of FASTQs, consult Specifying Input FASTQ Files for 10x Genomics pipelines.|
After determining these input arguments and customizing the code in red, run cellranger:
$ cd /home/jdoe/runs $ cellranger count --id=sample345 \ --transcriptome=/opt/refdata-gex-GRCh38-2020-A \ --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \ --sample=mysample \ --localcores=8 \ --localmem=64
Following a series of checks to validate input arguments, cellranger count pipeline stages will begin to run:
Martian Runtime - v4.0.8 Running preflight checks (please wait)... Checking sample info... Checking FASTQ folder... Checking reference... Checking optional arguments... ...
By default, Cell Ranger 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,
will limit Cell Ranger to using up to sixteen cores at once. Similarly,
--localmem will restrict the amount of memory (in GB) used by
The pipeline will create a new folder named with the sample ID you specified (e.g.
/home/jdoe/runs/sample345) for its output. If this folder already exists, Cell Ranger will assume it is an existing pipestance and attempt to resume running it.
A successful cellranger count run should conclude with a message similar to this:
Outputs: - Run summary HTML: /opt/sample345/outs/web_summary.html - Run summary CSV: /opt/sample345/outs/metrics_summary.csv - BAM: /opt/sample345/outs/possorted_genome_bam.bam - BAM index: /opt/sample345/outs/possorted_genome_bam.bam.bai - Filtered feature-barcode matrices MEX: /opt/sample345/outs/filtered_feature_bc_matrix - Filtered feature-barcode matrices HDF5: /opt/sample345/outs/filtered_feature_bc_matrix.h5 - Unfiltered feature-barcode matrices MEX: /opt/sample345/outs/raw_feature_bc_matrix - Unfiltered feature-barcode matrices HDF5: /opt/sample345/outs/raw_feature_bc_matrix.h5 - Secondary analysis output CSV: /opt/sample345/outs/analysis - Per-molecule read information: /opt/sample345/outs/molecule_info.h5 - CRISPR-specific analysis: null - Loupe Browser file: /opt/sample345/outs/cloupe.cloupe - Feature Reference: null - Target Panel File: null 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 sample ID you specified (e.g.
Once cellranger count has successfully completed, you can browse the resulting web summary HTML file in any supported web browser and open the
.cloupe file in Loupe Browser. Refer to the Understanding Outputs 3' Gene Expression Outputs page for descriptions about all output files.
|Required. A unique run ID string: e.g. |
Path of the
- OR -
Any folder containing fastq files, for example if the fastq files were generated by a service provider and delivered outside the context of the mkfastq output directory structure. See input FASTQ page for help.
||Path to a |
This argument should not be used when performing gene expression-only analysis; use
||Required. Sample name as specified in the sample sheet supplied to cellranger mkfastq.
|Required. Path to the Cell Ranger-compatible transcriptome reference e.g.,|
||Path to a Feature Reference CSV file declaring the Feature Barcode reagents used in the experiment. Required for Feature Barcode analysis.|
||Path to a Target Panel CSV file declaring the target panel used, if any. Required for Targeted Gene Expression analysis. Default analysis will exclude intronic mapped reads, which is the recommended mode for targeted assays.|
||Optional. Add this flag to disable targeted UMI filtering. See Targeted algorithms for details.|
||Optional. Override the pipeline’s auto-estimate. See cell calling algorithm overview for details on how this parameter is used. If used, enter the expected number of recovered cells.|
||Optional. Force pipeline to use this number of cells, bypassing the cell detection algorithm. Use this if the number of cells estimated by Cell Ranger is not consistent with the barcode rank plot.|
||Optional. Set to false to exclude intronic reads in count. Including introns in analysis is recommended to maximize sensitivity, except when |
||Optional. Add this flag to skip secondary analysis of the feature-barcode matrix (dimensionality reduction, clustering, and visualization). Set this if you plan to use cellranger reanalyze or your own custom analysis.|
||Optional. Set this flag to true to skip BAM file generation. This will reduce the total computation time for the pipestance and the size of the output directory. If unsure, we recommend not using this option, as BAM files can be useful for troubleshooting and downstream analysis. Default: false.|
||Optional. Proceed with processing using a
||Optional. Assay configuration.
NOTE: by default the assay configuration is detected automatically,
which is the recommended mode. You should only specify chemistry if there is an error in automatic detection. Select one of:
|Optional. Hard-trim the input R1 sequence to this length. Note that the length includes the Barcode and UMI sequences so do not set this below 26 for Single Cell 3′ v2 or Single Cell 5′. This and |
|Optional. Hard-trim the input R2 sequence to this length.|
|Optional. Lanes associated with this sample|
|Optional. Restricts cellranger to use specified number of cores to execute pipeline stages. By default, cellranger will use all of the cores available on your system.|
|Optional. Restricts cellranger to use specified amount of memory (in GB) to execute pipeline stages. By default, cellranger will use 90% of the memory available on your system.|
|Optional. This option allows users to disable the check that evaluates 10x Barcode overlap between libraries when multiple libraries are specified (e.g., Gene Expression + Antibody Capture). Setting this option to |