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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
• 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 TCR-Seq Analysis
    • The report output from Single Cell TCR-Seq Analysis
    • The Cell Clonotypes element
    • 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
• 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 (10xVDJ)
    • Output from Immune Repertoire Analysis from Reads (10xVDJ)
  • Immune Repertoire and Expression Analysis from Reads (10xVDJ)
    • Configuring the batch units for Immune Repertoire and Expression Analysis from Reads (10xVDJ)
    • Output from Immune Repertoire and Expression Analysis from Reads (10xVDJ)
• 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

The clonotype identification algorithm

The algorithm for identifying the clonotypes is composed of three sequential steps described below.

Assembly

All reads originating from the same barcode are collected and:

• Barcodes containing ambiguous nucleotides are discarded.
• Barcodes with less than 5 UMIs are discarded.
• Barcodes with more than 80,000 reads are down-sampled to about 80,000 reads.
• Remaining reads are de novo assembled into contigs.
• Contigs shorter than 60 nucleotides are discarded.
• The reads are mapped back to the valid contigs and the contigs are adjusted by the mapped reads.
• Contigs and barcodes of low quality are discarded. The following are required:
  • Contigs should have an average coverage of at least 5.
  • Contigs should have at least 20 mapped reads.
  • If more than four contigs are assembled, contigs should have an average coverage of at least the median average coverage of all contigs.
  • Barcodes should have at least 3 UMIs mapped to high-quality contigs.

The assemble summary reports:

• the number of input barcodes and reads;
• the number of processed barcodes (those without ambiguous nucleotides) and reads (those left after down-sampling);
• the number of barcodes that have been discarded;
• the number of barcodes and high-quality contigs that have been successfully assembled;

Trimming the C gene segments

The C gene segments need trimming prior to clonotype identification. The contigs are therefore trimmed with the following settings:

• The ends of the contigs are trimmed such that at most 2 ambiguous nucleotides are left in the contig.
• The C gene segments provided in the "Trim constant regions" as a Trim Adapter List are removed.
• Contigs shorter than 60 nucleotides after trimming are discarded.

The trimming summary reports the average length of the contigs before and after trimming, and how many barcodes and contigs remain after trimming.

Clonotype identification

T-cell receptors come in two varieties, either + or + T-cell receptors, with the + type being the far most abundant. Each chain is encoded by a gene that undergoes somatic recombination. In the somatic recombination process, gene segments are joined together with random nucleotides added at the junction sites. and chains are the result of V and J gene segments recombination, while and are the result of V, D and J gene segments recombination. During the recombination, the gene segments are joined together.

The V gene segment contains a conserved cysteine amino-acid marking the beginning of the CDR3 region and the J gene segment contains a conserved phenylalanine amino-acid marking the end of the CDR3 region. The CDR3 region is highly variable.

Clonotyping a contig consists of identifying which V and J gene segments are used and extracting the CDR3 region. The D gene segments are not identified here.

The identification of V and J gene segments is done by mapping the contigs against references containing all V or all J gene segments, provided in "Reference segments".

Depending on the length of the gene segment that is covered by the contig and the diversity of the gene segment, it might not be possible to unambiguously detect the gene segment. In this case, all possible gene segments are reported.

After the initial clonotyping of the contigs, merging of clonotypes is performed as follows:

• If a clonotype has an ambiguously assigned V and / or J, it will be merged, if possible, into a clonotype with the same CDR3 and unambiguous V and J assignment.
• If two clonotypes exist with the same V and J, but differing by a single nucleotide in the CDR3 sequence, the clonotype with fewer reads will be merged into the other.

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