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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
  • The QIAGEN Cell Ontology
  • Reference data management
    • The Reference Data Manager
  • Running tools and workflows
• Data import
  • Import Expression Matrix
  • Import Cell Annotations
  • Import Cell Clusters
  • Import Cell Clonotypes
• Data export
  • Export Cell Ranger HDF5 expression matrix
  • Export Loom expression matrix
  • Export MEX expression matrix
• Creating a count matrix from FASTQ
  • Annotate Reads with Cell and UMI
    • Setting the sample name
    • Interpreting the output of Annotate Reads with Cell and UMI
  • Single Cell RNA-Seq Analysis
    • The Single Cell RNA-Seq Analysis report
    • The Single Cell RNA-Seq Analysis algorithm
• Cell preparation
  • 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
  • Combine Cell Annotations
  • Combine Cell Clusters
  • Convert Metadata to Cell Annotations
  • Update to Common Sample Name
• Noise reduction through feature selection and PCA
  • Feature selection and PCA
    • Calculation of estimated biological variation
• Cell Annotation
  • 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
  • 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
  • Create UMAP Plot
  • Create tSNE Plot
  • Add Information to Plot
• 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
• Immune Repertoire
  • Single Cell Immune Repertoire Analysis
    • The report output from Single Cell Immune Repertoire Analysis
    • The Cell Clonotypes element
    • The clonotype identification algorithm
  • Filter Cell Clonotypes
  • Combine Cell Clonotypes
  • Compare Single Cell Immune Repertoires
    • Interpreting the output of Compare Single Cell Immune Repertoires
  • Convert Clonotypes to Cell Annotations
• Single cell workflows from reads
  • Expression Analysis from Reads
    • Configuring the batch units
    • Output from Expression Analysis from Reads
  • Immune Repertoire Analysis from Reads (10xVDJ)
    • Output from Immune Repertoire Analysis from Reads (10xVDJ)
  • Immune Repertoire and Expression Analysis from Reads (10xVDJ)
    • Configuring the batch units with multiple input types
    • Output from Immune Repertoire and Expression Analysis from Reads (10xVDJ)
• Single cell workflows from imported data
  • Expression Analysis from Matrix
    • Output from Expression Analysis from Matrix
  • Immune Repertoire and Expression Analysis from Clonotypes and Matrix
    • Output from Immune Repertoire and Expression Analysis from Clonotypes and Matrix
• UMAP and tSNE plot functionality
  • Manual Annotation
  • Create Subset
  • Extract to Table
  • Launching of Create Expression Plot
  • Launching of Create Heat Map for Cell Abundance
  • Launching of Differential Expression for Single Cell
• 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

The report output from Single Cell Immune Repertoire Analysis

The optional report includes the following information 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 and J usage. Bar plots showing the V and J 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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