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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 Plain Text Table Expression Matrix
  • Export Peak Count Matrix
• Prepare Reads
  • Annotate Single Cell Reads
    • Read structure
    • Barcodes from names
    • Barcode correction
    • Sample name
    • 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
    • The output of QC for Single Cell
    • Choosing barcodes to retain
    • Cell calling
    • Automatic thresholds
    • Doublet calling
  • Demultiplex Parse Bio Samples
  • Normalize Single Cell Data
    • When is batch correction appropriate?
    • The output of Normalize Single Cell Data
    • The Normalize Single Cell Data algorithm
  • The Expression Matrix element
• Cell Type Classification
  • Browse QIAGEN Cell Ontology
  • Predict Cell Types
    • The output of Predict Cell Types
    • Cell type refinement
  • Train Cell Type Classifier
    • Features used for training and prediction
    • 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
    • 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
    • The output of Single Cell Velocity Analysis
    • The velocity estimation algorithm
  • Differential Velocity for Single Cell
  • Score Velocity Genes
    • The output of Score Velocity Genes
  • Create Phase Portrait Plot
  • Velocity analysis in workflows
  • The Velocity Matrix element
• Spatial Transcriptomics
  • The Spatial Transcriptomics Plot element
• Chromatin Accessibility
  • Single Cell ATAC-Seq Analysis
    • 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
  • The Peak Count Matrix element
• Immune Repertoire
  • Single Cell V(D)J-Seq Analysis
    • The report output from Single Cell V(D)J-Seq Analysis
    • The clonotype identification algorithm
  • Filter Cell Clonotypes
  • Combine Cell Clonotypes
  • Compare Cell Clonotypes
    • The output of Compare Cell Clonotypes
  • Convert Clonotypes to Cell Annotations
  • The Cell Clonotypes element
    • Primary and secondary clonotypes
    • Cell Clonotypes tables
    • Cell Clonotypes alignments
    • Cell Clonotypes Sankey plot
• 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
    • The output of Create Heat Map for Cell Abundance
  • Create Cell Annotations from Hashtags
  • The Cell Annotations element
  • The Cell Clusters element
• 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

Doublets filter

Certain single-cell protocols can assign the same barcode to two or more cells. For example, in droplet-based data, each droplet has a unique barcode, but droplets can contain more than one cell. For data obtained using combinatorial barcoding, there is a chance that two cells will travel together through all barcoding rounds, creating a doublet.

In this dialog of QC for Single Cell, the Doublets filter can be enabled and customized to remove the barcodes that are detected as being assigned to two cells. This filter should be skipped for single-cell protocols that do not generate doublets.

Note that QC for Single Cell cannot remove barcodes that are assigned to 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 7.8: The default settings in the Doublets filter dialog.

The following options can be adjusted in the Doublets filter dialog (figure 7.8):

• Identify and remove barcodes assigned to two cells. Enables filtering of the doublets. This should be unchecked for single-cell protocols that do not generate doublets.
• 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.
• Platform. The percentage of barcodes that are expected to be doublets depends on the platform used for generating the single-cell data. Choosing the platform sets the default values for Expected doublets (%) and Correction margin (%), even though Specify expected doublets is not checked (see below). The following options are available:
  • 10x Geonomics. Expected doublets (%) is set to 1% per 1000 captured cells, and Correction margin (%) is set to half of this.
  • Parse Biosciences. Expected doublets (%) is set differently according to the kit size:
    • 3% for at most 10,000 captured cells,
    • 5% for at most 100,000 captured cells,
    • 7% otherwise.
    Correction margin (%) is set to half of the Expected doublets (%).
  • Other. No default values are used. It is recommended to specify the expected doublets by checking Specify expected doublets.
• Specify expected doublets. Check 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.
• Correction margin (%). The percentage of predicted doublets will lie in the interval given by `Expected doublets (%)' `Correction margin (%)'.

For more details on how doublets are detected, see Doublet calling.

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