Immune Repertoire and Expression Analysis from Reads (10xVDJ)

The workflow Immune Repertoire and Expression Analysis from Reads (10xVDJ) takes Reads as input and jointly analyzes scRNA-seq and scTCR-seq data originating from the same sample. The reads are first annotated with cell barcode and UMI, after which they are sent on two different paths, one for each type of data. The workflow splits the reads according to sample and data type, as given through metadata.

The scRNA-seq and scTCR-seq paths follow the same analysis described in Expression Analysis from Reads and Immune Repertoire Analysis from Reads (10xVDJ), respectively.

Everything is then collected to produce:

The workflow can be found in the toolbox here:

        Workflows (Image sc_workflow_folder_open_16_n_p) | From Reads (Image sc_wf_from_reads_folder_open_16_n_p) | Immune Repertoire and Expression Analysis from Reads (10xVDJ) (Image sc_tcr_rna_from_reads_16_n_p)

If you are connected to a CLC Server via your Workbench, you will be asked where you would like to run the analysis. We recommend that you run the analysis on a CLC Server when possible.

You can choose either one or more Sequence lists or Select files for import and select FASTQ files for importing.

The workflow is configured for 10x Chromium Single Cell V(D)J data. For the scRNA-seq path, a number of options are customizable, see Expression Analysis from Reads. For the scTCR-seq path, only clonotype filtering is customizable, see Immune Repertoire Analysis from Reads (10xVDJ). Adjustments can be made in a workflow copy, see https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Editing_existing_workflows.html.

The workflow can be run using Single Cell hg38 (Ensembl) or Single Cell Mouse (Ensembl) reference data sets (see The Reference Data Manager).

Note: Reference data elements cannot be configured during workflow execution. If other elements than those provided in the default reference data sets are needed a custom reference data set can be used, see  https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Custom_Sets.html. When creating custom reference data sets, the chosen gene track needs to match the gene annotations used for training the provided Cell Type Classifier (Image cell_type_classifier_16_n_p), see Features used for training and prediction.

The workflow allows the analysis of multiple samples and you must specify metadata during execution for configuring which reads belong to which sample and data type, see Configuring the batch units with multiple input types.



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