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• Introduction
  • The concept of CLC Single Cell Analysis Module
  • Contact information
  • System requirements and installation
    • System requirements
    • Installation of modules
    • Uninstalling modules
  • Licensing modules
    • Server License
  • Reference data management
    • The Reference Data Manager
  • Running tools and workflows
• Data import
  • Import Expression Matrix
  • Import Peak Count Matrix
  • Import Cell Annotations
  • Import Cell Clusters
  • Import Cell Clonotypes
  • Cell format in importers
  • Import Immune Reference Segments
    • IMSEQ
    • IMGT
    • Output from Import Immune Reference Segments
• Data export
  • Export Cell Ranger HDF5 Expression Matrix
  • Export Loom Expression Matrix
  • Export MEX Expression Matrix
  • Export TXT Expression Matrix
  • Export Peak Count Matrix
• Prepare Reads
  • Annotate Reads with Cell and UMI
    • Setting the sample name
    • Interpreting the output of Annotate Reads with Cell and UMI
• Creating a Gene Expression Matrix
  • Single Cell RNA-Seq Analysis
    • The Single Cell RNA-Seq Analysis report
    • The Single Cell RNA-Seq Analysis algorithm
  • QC for Single Cell
    • Empty droplets filter
    • Count-based and extra-chromosomal filters
    • Doublets filter
    • Interpreting the output of QC for Single Cell
    • Choosing barcodes to retain
    • The cell calling algorithm
    • The doublet calling algorithm
  • Normalize Single Cell Data
    • When is batch correction appropriate?
    • Interpreting the output of Normalize Single Cell Data
    • The Normalize Single Cell Data algorithm
• Cell Type Classification
  • Browse QIAGEN Cell Ontology
  • Predict Cell Types
  • Train Cell Type Classifier
    • Features used for training and prediction
    • Interpreting the output of Train Cell Type Classifier
    • SVMs for cell type classification
• Expression Analysis
  • Differential Expression for Single Cell
    • Interpreting the output of Differential Expression for Single Cell
    • The differential expression algorithm
  • Create Expression Plot
    • The Heat Map output of Create Expression Plot
    • The Dot Plot output of Create Expression Plot
    • The Violin Plot output of Create Expression Plot
• Velocity Analysis
  • Single Cell Velocity Analysis
    • Interpreting the output of Single Cell Velocity Analysis
    • The velocity estimation algorithm
  • Differential Velocity for Single Cell
  • Score Velocity Genes
    • Interpreting the output of Score Velocity Genes
  • Create Phase Portrait Plot
• Chromatin Accessibility
  • Single Cell ATAC-Seq Analysis
    • Interpreting the output of Single Cell ATAC-Seq Analysis
    • The report output from Single Cell ATAC-Seq Analysis
    • The Single Cell ATAC-Seq Analysis algorithm
  • Split Read Mapping By Cell
  • Differential Accessibility for Single Cell
    • The differential accessibility algorithm
• Immune Repertoire
  • Single Cell V(D)J-Seq Analysis
    • The report output from Single Cell V(D)J-Seq Analysis
    • The clonotype identification algorithm
  • Filter Cell Clonotypes
  • Combine Cell Clonotypes
  • Compare Cell Clonotypes
    • Interpreting the output of Compare Cell Clonotypes
  • Convert Clonotypes to Cell Annotations
    • Cell Annotations for TCR Cell Clonotypes
    • Cell Annotations for BCR Cell Clonotypes
  • The Cell Clonotypes element
    • Cell Clonotypes tables
    • Cell Clonotypes alignments
    • Cell Clonotypes Sankey plot
• Noise reduction through feature selection and dimensionality reduction
  • Feature selection and dimensionality reduction
    • Calculation of estimated biological variation
• Cell Annotation
  • Cluster Single Cell Data
    • The Cluster Single Cell Data algorithm
  • Create Heat Map for Cell Abundance
    • Interpreting the output of Create Heat Map for Cell Abundance
• Dimensionality reduction
  • UMAP for Single Cell
  • tSNE for Single Cell
• Utility tools
  • Combine Cell Annotations
  • Combine Cell Clusters
  • Convert Metadata to Cell Annotations
  • Add Information to Plot
  • Update to Common Sample Name
• Single cell template workflows from reads
  • Expression Analysis from Reads
    • Configuring the batch units for Expression Analysis from Reads
    • Output from Expression Analysis from Reads
  • Chromatin Accessibility Analysis from Reads
    • Configuring the batch units for Chromatin Accessibility Analysis from Reads
    • Output from Chromatin Accessibility Analysis from Reads
  • Chromatin Accessibility and Expression Analysis from Reads
    • Configuring the batch units for Chromatin Accessibility and Expression Analysis from Reads
    • Output from Chromatin Accessibility and Expression Analysis from Reads
    • Importing reads
  • Immune Repertoire Analysis from Reads (10xV(D)J)
    • Output from Immune Repertoire Analysis from Reads (10xV(D)J)
  • Immune Repertoire and Expression Analysis from Reads (10xV(D)J)
    • Configuring the batch units for Immune Repertoire and Expression Analysis from Reads (10xV(D)J)
    • Output from Immune Repertoire and Expression Analysis from Reads (10xV(D)J)
• Single cell template workflows from imported data
  • Expression Analysis from Matrix
    • Output from Expression Analysis from Matrix
  • Chromatin Accessibility and Expression Analysis from Matrix
    • Output of Chromatin Accessibility and Expression Analysis from Matrix
  • Immune Repertoire and Expression Analysis from Clonotypes and Matrix
    • Output from Immune Repertoire and Expression Analysis from Clonotypes and Matrix
  • Importing data
• UMAP and tSNE plot functionality
  • Manual Annotation
  • Visualizing Different Types of Matrices
  • Create Subset
  • Extract to Table
  • Launching of Create Expression Plot
  • Launching of Create Heat Map for Cell Abundance
  • Launching of Differential Accessibility for Single Cell
  • Launching of Differential Expression for Single Cell
  • Launching of Differential Velocity for Single Cell
  • Launching of Score Velocity Genes
• Licensing requirements for the CLC Single Cell Analysis Module
  • Licensing modules on a Workbench
    • Request an evaluation license
    • Download a license using a license order ID
    • Import a license from a file
    • Configure License Server connection
    • Download a static license on a non-networked computer
  • Licensing Server Extensions on a CLC Server
    • Download a static license on a non-networked machine
• Bibliography

Cell Clonotypes alignments

The alignments view () shows all assembled contigs mapping to a specific clonotype (figure 10.10).

Figure 10.10: Read mapping for the TRB clonotype from a clonotype containing both TRA and TRB. V, D, J and C segments are annotated on the reference sequence and the CDR3 is annotated on the consensus.

The alignment contains:

• The reference sequence consisting of the identified V(D)JC segments. Annotations indicate the location of the different segment types. For clonotypes with ambiguous segments, only one of the identified segments is used.
• The consensus sequence with an annotation indicating the CDR3 region.
• The aligned contigs.

The clonotypes for which the alignment should be shown can be selected from the drop-down menus in the Side Panel, or from one of the clonotype tables (Cell Clonotypes tables) while using a split view (figure 10.11).

Figure 10.11: Clonotypes split view. Top: multiple clonotypes sharing reference segments are selected in the table view. Bottom: Alignment view for the clonotypes selected in the table view.

Alignments for multiple clonotypes can be shown together provided that they have the same chain, V and J segments and the D / C segments are not contradictory: either the D / C segment is identified and the same, or it is missing (figure 10.11).

When viewing alignments for multiple clonotypes, it can be useful to change "Compactness" to "Not compact" and tick the "Sample", "Barcode", "Cell-level clonotype #" and "Clonotype #" from the Side Panel. This way, it is easy to see this information for each of the aligned contigs (figure 10.12)).

Figure 10.12: Alignment view for multiple clonotypes where "Compactness" is set to "Not compact" and "Clonotype #" is ticked.

Figure 10.11 shows an alignment for multiple clonotypes. Some of the contigs do not span past the J segment and using the "Clonotype #" annotation (figure 10.12)), we can confirm that these contigs belong to clonotypes for which the C segment has not been identified. Some of the clonotypes share the D segment, while others do not have an identified D segment. They all have different CDR3 sequences. Using the alignment view, it is straightforward to spot the differences between the CDR3 sequences.

For further processing, the alignments can be opened and saved as a stand-alone read mapping by using the "Open as Read Mapping" button. Using the Extract Reads tool (see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Extract_Reads.html), the contigs can be extracted from the stand-alone read mapping as a sequence list (), which can be used as input to Single Cell V(D)J-Seq Analysis. The tool will then skip the assembly and trimming and only clonotype the contigs (see The clonotype identification algorithm). This allows for custom processing of the contigs, where additional trimming can be performed before clonotyping.

Various settings controlling how the alignment, consensus and reference are displayed can be configured in the Side Panel, see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=View_settings_in_Side_Panel.html.

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