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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 Cell Annotations
  • Import Cell Clusters
• 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
    • 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
    • Interpreting the output of QC for Single Cell
    • Choosing barcodes to retain
    • The cell 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
• 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
    • Interpreting the output of Train Cell Type Classifier
    • Features used for training and prediction
    • SVMs for cell type classification
  • Cluster Single Cell Data
    • The Cluster Single Cell Data algorithm
• 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
  • Create Expression Plot
    • The Heat Map output of Create Expression Plot
    • The Dot Plot output of Create Expression Plot
• Single cell workflows
  • Perform Single Cell Analysis from Expression Matrix
    • Output from Perform Single Cell Analysis from Expression Matrix
  • Perform Single Cell Analysis from Reads
    • Configuring the batch units
    • Output from Perform Single Cell Analysis from Reads
• UMAP and tSNE plot functionality
  • Manual Annotation
  • Create Subset
  • Extract to Table
  • Launching of Create Expression Plot
  • 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

Import Cell Clusters

The Import Cell Clusters tool can import clusters for each cell. The importer produces Cell Clusters () that can be used to define groups of cells for use in many tools such as Differential Expression for Single Cell. Cell Clusters can also be visualized in a Dimensionality Reduction Plot.

Often the same file can be imported as either Cell Clusters or Cell Annotations. The principal advantage of Cell Clusters is that they can be edited within the Dimensionality Reduction Plot.

The importer can be found here:

        Import () | Import Cell Clusters ().

The following options are available:

• Data file a single file in .csv or .xlsx format. The first row in the file is used as the name of the columns of the imported table. Each subsequent row in the file should describe a cell (though empty lines are ignored). Cells are identified by a combination of their barcode e.g. "AAGCT" and their sample id. The first column in the file will be treated as the sample id, and the second column will be treated as the barcode. Subsequent columns are different clusterings of the cells (see figure 2.5). When an Expression Matrix is supplied, the sample id column may be omitted.

  
  Figure 2.1: An example of a file that could be imported with Import Cell Clusters. The file contains three different clusterings.

• Expression Matrix (optional when First column defines sample is selected). When an Expression Matrix is supplied:
  • The sample id is taken from the matrix. If the file provides different sample ids than the matrix, then the importer fails with an error. This can be useful when checking that the file being imported matches the supplied matrix.
  • Rows in the file describing cells that are not in the matrix are skipped.
• First column defines sample When this is enabled, there is no need to supply an Expression Matrix. However, the data file must have the sample id in the first column.
• Map clusters to QIAGEN Cell Ontology When this is enabled, each annotation will be translated to the QIAGEN Cell Ontology. The translation works by looking for known synonyms for each cell type in the ontology. For example, `Sertoli cells' are also called `nurse cells'. If the data file annotates cells as `nurse cells', and this option is selected, then in the imported file, the cells will be called `Sertoli cells' (see figure 2.6). This option can be useful when standardizing cell annotations from different sources. It is especially recommended if the imported data will be used to extend a QIAGEN Cell Type Classifier using the Train Cell Type Classifier tool.

Figure 2.2: The result of importing the file shown in figure 2.5 using the option `Map clusters to QIAGEN Cell Ontology'. Note that all the cell types have been translated to terms in the QIAGEN Cell Ontology. For example, `T cells' have been standardized to `T lymphocytes', and `nurse cells' have been standardized to `Sertoli cells'.

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