Cell Ranger7.1 (latest), printed on 11/21/2024
After June 30, 2023, new Cell Ranger releases will no longer support Targeted Gene Expression analysis. |
Targeted Gene Expression technology uses selected panels of relevant targeted genes. When analyzing Targeted Gene Expression data, Cell Ranger requires a Target Panel CSV file as an input file.
Target Panel CSV files corresponding to predesigned gene panels can be found within the target_panels/
directory in the Cell Ranger package. For example, if you installed Cell Ranger to /opt/cellranger-5.0.0
, then the target panel CSVs will be contained in /opt/cellranger-5.0.0/target_panels
. These CSV files can also be downloaded on the Panel Selection page, along with more detailed information about each panel.
Detailed documentation on this file format can be found here along with descriptions of all other downloads provided for target gene panels.
Cell Ranger performs analysis of single cell Targeted Gene Expression data with the cellranger count and multi pipelines. Cell Ranger provides the same interface and features for both Targeted and Whole Transcriptome Analysis (WTA) Gene Expression data, as described in Single-Library Analysis with Cell Ranger.
To enable Targeted Gene Expression analysis in cellranger count, specify the relevant Target Panel CSV file using the --target-panel
flag:
cd /home/jdoe/runs cellranger count --id=sample345 \ --target-panel=/opt/cellranger-5.0.0/target_panels/immunology_v1.0_GRCh38-2020-A.target_panel.csv \ --transcriptome=/opt/refdata-gex-GRCh38-2020-A \ --fastqs=/home/jdoe/runs/HAWT7ADXX/outs/fastq_path \ --sample=mysample \ --localcores=8 \ --localmem=64
The default analysis for targeted assays will exclude intronic mapped reads. Excluding intronic mapped reads is the recommended analysis path for targeted data because 10x Genomics-supported baits for this assay are designed to capture exons only. |
For Targeted Gene Expression data, targeted UMI filtering is performed after the usual UMI Counting step. This additional filtering is only active for sequencing libraries with very high depth, in which spurious molecules can be observed in a very small fraction of reads. Targeted UMI filtering can be disabled when --target-panel
is used with the --no-target-umi-filter
flag, although this is not recommended.
The outputs 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.
To analyze Targeted Gene Expression with Antibody Capture or CRISPR Guide Capture libraries, you can use the cellranger count pipeline. See Feature Barcode Analysis for details about generating the libraries CSV and the feature reference CSV files. In addition to the flag specifying the target panel file described above, you'll also need to add flags for Feature Barcode information. For this analysis, the FASTQ file paths and sample information are specified in the libraries CSV file. For example (replace text in red with correct file paths):
cellranger count --id=sample345 \ --target-panel=/opt/cellranger-5.0.0/target_panels/immunology_v1.0_GRCh38-2020-A.target_panel.csv \ --transcriptome=/opt/refdata-gex-GRCh38-2020-A \ --libraries=/path/to/library.csv \ --feature-ref=/path/to/feature_ref.csv \ --localcores=8 \ --localmem=64
To analyze Targeted Gene Expression with 3' Cell Multiplexing libraries, use the cellranger multi pipeline. An example dataset is available for multiplexed mouse samples sequenced with a target neuroscience gene panel. See Cell Multiplexing with cellranger multi for details about generating the multi config CSV file. For example (replace text in red with correct file paths):
[gene-expression] reference,/path/to/refdata-gex-mm10-2020-A target-panel,/path/to/target_panel.csv [libraries] fastq_id,fastqs,feature_types targeted_gex,/path/to/fastqs,Gene Expression cellplex,/path/to/fastqs,Multiplexing Capture [samples] sample_id,cmo_ids sample1,CMO301 sample2,CMO304
Then, run Cell Ranger:
cellranger multi --id=sample345 --csv=/path/to/multi_config_csv.csv