• Product manuals

Browse the manual

• 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

Doublets filter

In droplet-based data, barcodes can correspond to droplets containing two or more cells. In this dialog of QC for Single Cell, the Doublets filter can be enabled and customized to remove the droplets that are detected as containing two cells. This filter should be skipped for single-cell protocols that are not droplet-based.

Note that QC for Single Cell cannot remove droplets containing more than two cells. However, these are expected to be present at negligible rates.

There are two types of doublets:

• homotypic doublets are formed by two cells with similar expression profiles;
• heterotypic doublets are formed by two cells with different expression profiles.

Doublet-removal software, which relies on gene expression to detect doublets, cannot identify homotypic doublets, as their expression profiles are indistinguishable from those of other cells. Alternative approaches are required to detect homotypic doublets, such as cell hashing [Stoeckius et al., 2018] and SNPs in multiplexed samples [Kang et al., 2018].

The Doublets filter simulates heterotypic doublets by averaging the expression of two random barcodes that are sufficiently different from each other. These artifical doublets are then used for predicting which of the input barcodes are doublets.

Figure 5.4: The default settings in the Doublets filter dialog.

The following options can be adjusted in the Doublets filter dialog (figure 5.4):

• Identify and remove barcodes from droplets containing two cells. Enables filtering of the doublets. This should be disabled for single-cell protocols that are not droplet-based.
• PCA dimensions. The number of PC dimensions to be used when reducing the dimensions of the expression data.
• Neighborhood size (%). Simulated doublets are obtained from barcodes that are not in each other's neighborhood. The size of the neighborhood is specified as % of input barcodes. Note that this is relative to the number of barcodes that pass all previous filters of QC for Single Cell. The optimal neighborhood size is data-set specific and would typically depend on the number of clusters in the data.
• Specify expected doublets. Enable this option to specify approximately how many doublets are present in the data. This option should be used whenever a reasonable expectation is known, as it is very important for an accurate detection of doublets.
• Expected doublets (%). The percentage of barcodes that are expected to be doublets, relative to the number of captured cells. If 'Specify expected doublets' is disabled, this is set to per captured cells, which is roughly the doublet rate for 10x data.
• Correction margin (%). The percentage of predicted doublets will lie in the interval given by 'Expected doublets (%)' 'Correction margin (%)'. If 'Specify expected doublets' is disabled, this is set to half of the value of 'Expected doublets (%)'.

For more details on how doublets are detected, see The doublet calling algorithm.

---