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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

The Dot Plot output of Create Expression Plot

A Dot Plot summarizes the expression of all the cells in a grouping for each gene (see figure 9.3). You may need to scroll downwards or to the right to view all the data in the plot. Alternatively Export Graphics can be used to export the entire plot in an image format such as png, for more details see https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Export_graphics_files.html.

In a Dot Plot, the expression values are z-score normalized like they are in the Heat Map. This allows the relative expression of genes with very different average expressions to be visualized in the same plot, but means that expression values cannot be compared between genes - only between cells for the same gene.

Figure 9.3: A Dot Plot visualization of data from [MacParland et al., 2018].

Each combination of gene and cell grouping is represented by a circle whose diameter is proportional to the percentage of cells in the grouping that express that gene. Note that scaling by diameter means that a gene expressed in 50% of cells will have one quarter the area of a gene expressed in 100% of cells. Good marker genes will typically be present in a large percentage of the cells for a cell type.

From the Side Panel, it is possible to change the values by which cells are colored. The options are:

Average expression

The average expression of cells with at least some expression of the gene.

Median expression

The median expression of cells with at least some expression of the gene. The median is more robust than the average in the sense that its value is less affected by outliers.

Variance of expression

The sample variance of cells with at least some expression of the gene. In some cases, when a grouping of cells has high variance for a gene, then this may be evidence that the grouping contains more than one population of cells. Otherwise it may indicate that the gene's expression changes rapidly.

The order in which the clusters or genes are arranged is adjustable in the Features and Groups selection. Click the green plus and use the arrows to remove, add or rearrange the order of the visualized genes or clusters. The coloring can also be changed by clicking the color gradient in the Side Panel. The relative coloring of the values can be adjusted by dragging the two knobs on the white slider above the color gradient.

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