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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 TXT Expression Matrix
  • Export Peak Count Matrix
• Prepare Reads
  • Annotate Single Cell Reads
    • Setting the sample name
    • Interpreting 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
    • 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
    • Interpreting the output of Predict Cell Types
    • Cell type refinement
  • Train Cell Type Classifier
    • Features used for training and prediction
    • Interpreting 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
    • 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
  • Velocity analysis in workflows
• Spatial Transcriptomics
  • The Spatial Transcriptomics Plot element
• 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
  • The Cell Clonotypes element
    • Primary and secondary clonotypes
    • Cell Clonotypes tables
    • Cell Clonotypes alignments
    • Cell Clonotypes Sankey plot
  • Filter Cell Clonotypes
  • Combine Cell Clonotypes
  • Compare Cell Clonotypes
    • Interpreting the output of Compare Cell Clonotypes
  • Convert Clonotypes to Cell Annotations
• 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
  • Create Cell Annotations from Hashtags
• 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

The report output from Single Cell V(D)J-Seq Analysis

The optional report includes information for different chain types:

• For TCR Cell Clonotypes: TRA + TRB, TRA, TRB, TRG + TRD, TRG, and TRD;
• For BCR Cell Clonotypes: IGH + IGK, IGH + IGL, IGH, IGK, and IGL.

Only the chain types that are found in the Cell Clonotypes are present in the report.

The following information is provided for each different chain type:

• Summary. Summary tables with information about the performed assembly, trimming and identified clonotypes. See The clonotype identification algorithm for more details.
• Diversity indices. Several diversity indices, as listed below. The extrapolated diversity gives a projection of what the diversity would have been if the sample had been sequenced deeply enough to identify all clonotypes.
  • Distinct clonotypes: The number of different clonotypes detected.
  • Extrapolated diversity (chaoE): The extrapolated number of detected distinct clonotypes as described in [Chao, 1987].
  • Lorenz curve at 50% of total: The fraction of all detected clonotypes that account for 50% of the total count. Also sometimes denoted as D50.
  • Inverse Simpson's index: Let denote the count for the th distinct clonotype and let . Then the inverse Simpon's index is defined as:
    

  • Extrapolated Inverse Simpson's index (chaoE): The extrapolated inverse Simpson's index as described in [Chao et al., 2014].
  • Shannon-Wiener index: With and defined as above, the Shannon-Wiener index is defined as:
    

  • Extrapolated Shannon-Wiener index (chaoE): The extrapolated Shannon-Wiener index as described in [Chao et al., 2013].
• Rarefaction. Rarefaction curves, also known as species accumulation curves. They show the expected number of distinct clonotypes discovered as a function of the total number of detected clonotypes, together with the confidence interval (CI), obtained from a normal approximation. The curve is
  • interpolated down to 0 clonotypes;
  • extrapolated to twice the total number of detected clonotypes.
• CDR3 length. The distribution of the length of the CDR3 nucleotide sequences for all detected clonotypes. Peaks are expected every 3 nucleotides due to repertoires consisting predominantly of in-frame CDR3 sequences.
• V, D, J and C usage. Bar plots showing the V, D, J and C segment usage for all detected clonotypes.
• Frequencies. The percentage of all detected clonotypes that are unique and the clonotype abundance: how many distinct clonotypes are found with abundance (count) . Most clonotypes are expected to be unique, so the percentage is close to 100% and most clonotypes have abundance 1.
• Productive summary. The percentage of all detected clonotypes that have productive CDR3 nucleotide sequences, and the percentage of barcodes with at least one productive CDR3 nucleotide sequence.

Note that for diversity indices and rarefaction, the number of distinct clonotypes is used. For the rest of the report, the number of detected clonotypes contains all clonotypes for all barcodes, where if more than one barcode has the same clonotype, this is counted multiple times.

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