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• Introduction
  • The concept of CLC Single Cell Analysis Module
  • Contact information
  • System requirements
  • Installing modules
    • Licensing modules
    • Uninstalling modules
  • Installing server extensions
    • Licensing server extensions
• Reference data management
  • QIAGEN Sets
• Running tools and workflows
• Data import
  • Import Cell Annotations
  • Import Cell Clusters
  • Import Cell Clonotypes
  • Import Expression Matrix
    • HDF5 formats
    • Other formats
  • Import Peak Count Matrix
  • Import Space Ranger
  • 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 AnnData Expression Matrix
  • Export h5Seurat Expression Matrix
  • Export Loom Expression Matrix
  • Export MEX Expression Matrix
  • Export Plain Text Table Expression Matrix
  • Export Peak Count Matrix
• Prepare Reads
  • Annotate Single Cell Reads
    • Read structure
    • Barcodes from names
    • Barcode correction
    • Sample name
    • The output of Annotate Single Cell Reads
• 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
    • The output of QC for Single Cell
    • Choosing barcodes to retain
    • Cell calling
    • Automatic thresholds
    • Doublet calling
  • Demultiplex Parse Bio Samples
  • Normalize Single Cell Data
    • When is batch correction appropriate?
    • The output of Normalize Single Cell Data
    • The Normalize Single Cell Data algorithm
  • The Expression Matrix element
• Cell Type Classification
  • Browse QIAGEN Cell Ontology
  • Predict Cell Types
    • The output of Predict Cell Types
    • Cell type refinement
  • Train Cell Type Classifier
    • Features used for training and prediction
    • The output of Train Cell Type Classifier
    • SVMs for cell type classification
  • Update Cell Type Classifier
  • The Cell Type Classifier element
• Expression Analysis
  • Differential Expression for Single Cell
    • 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
    • The output of Single Cell Velocity Analysis
    • The velocity estimation algorithm
  • Differential Velocity for Single Cell
  • Score Velocity Genes
    • The output of Score Velocity Genes
  • Create Phase Portrait Plot
  • Velocity analysis in workflows
  • The Velocity Matrix element
• Spatial Transcriptomics
  • The Spatial Transcriptomics Plot element
• Chromatin Accessibility
  • Single Cell ATAC-Seq Analysis
    • 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
  • The Peak Count Matrix element
• 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
    • The output of Compare Cell Clonotypes
  • Convert Clonotypes to Cell Annotations
  • The Cell Clonotypes element
    • Primary and secondary clonotypes
    • 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
    • The output of Create Heat Map for Cell Abundance
  • Create Cell Annotations from Hashtags
  • The Cell Annotations element
  • The Cell Clusters element
• Dimensionality reduction
  • UMAP for Single Cell
  • tSNE for Single Cell
• Single cell low-dimensional plots 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
• Utility tools
  • Combine Cell Annotations
  • Update Cell Annotations
  • Convert Metadata to Cell Annotations
  • Combine Cell Clusters
  • Update Cell Clusters
  • Add Information to Plot
  • Update Single Cell 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
• Bibliography

Cell Clonotypes tables

The Cell Clonotypes elements contain two table views, centered around the clonotypes () and barcodes (). Multiple clonotypes can be identified for a barcode, where each clonotype is for one chain. These are the cell-level clonotypes (). Clonotypes () can have more than one chain (see below). For a given chain, the clonotype consists of multiple cell-level clonotypes with the same characteristics. The different chains present in one clonotype are supported by the corresponding cell-level clonotypes being present in the same barcode.

Both views contain the following information (see figure 13.8):

Figure 13.8: Views of the same TCR Cell Clonotypes element. Note that not all table columns are shown. Clonotype with number 9 is highlighted in both views. Top: View centered around the identified clonotypes, sorted after the number of barcodes. All identified chains for the clonotype are shown. For example, clonotype 9 contains both a TRA and TRB chain, while clonotypes 10 and 11 contain only a TRB and TRA chain, respectively. When a clonotype is selected, a second table lists the barcodes with the corresponding clonotype. Bottom: View centered around the barcodes, sorted by barcode. Rows with the same barcode have the same background color when the table is sorted after the barcode.

• Clonotype #. A unique number identifying the clonotype.
• Chain: Which chain the clonotype belongs to. Can be:
  • For TCR Cell Clonotypes: TRA, TRB, TRA + TRB, TRG, TRD, and TRG + TRD;
  • For BCR Cell Clonotypes: IGH, IGK, IGL, IGH + IGK, and IGH + IGL.
  Cell-level clonotypes have only one chain.
• V / D / J / C. The identified V, D, J and C reference segment(s), respectively. If a single unambiguous segment cannot be identified, the segments are separated by a comma.
• CDR3 nucleotide sequence. The nucleotide sequence for CDR3 including the V- and J region-encoded conserved motifs.
• CDR3 amino acid sequence. The translated amino acid sequence for the CDR3 nucleotide sequence, provided that it is in-frame.
• CDR3 length. The length of the CDR3 nucleotide sequence.
• Productive. One of three categories are used to characterize the CDR3 nucleotide sequence:
  • Productive. Sequences that are in frame and do not contain a premature stop codon.
  • Out-of-frame. Sequences that have a length that is not a multiple of three.
  • Premature stop codon. Sequences that contain an in-frame premature stop codon.

  Note that the Filter Cell Clonotypes can be used for retaining only the productive clonotypes, see Filter Cell Clonotypes for details.

The view centered around the identified clonotypes () additionally contains:

• Barcodes. The number of barcodes with the given clonotype.
• Primary (%). The percentage of clonotypes that were the primary clonotype for their respective barcode, see Primary and secondary clonotypes.

Clicking on a row in this view opens a new table listing the corresponding barcodes (see figure 13.8).

The cell-level clonotypes view () also provides (see figure 13.8):

• Cell-level clonotype #. A unique number identifying the barcode and clonotype.
• Sample / Barcode. The sample and barcode.
• Reads. The number of reads from the barcode that mapped to the contig from which the specific CDR3 sequence was detected.
• UMIs. The number of unique UMIs the aligned reads correspond to.
• Copy rank. Primary, Secondary or Subsequent, see Primary and secondary clonotypes.

To create a new Cell Clonotypes element from a row selection in a Cell Clonotypes table, use the Create Clonotypes from Selection option in the right-click menu.

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