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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
  • 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

The Violin Plot output of Create Expression Plot

Violin plots superimpose a kernel density plot on a box plot in order to provide more insight into the distribution of expressions in a sample. The box plot shows the median as a filled black square, the interquartile ranges as an unfilled black box, and the range of other non-outlier measurements as whiskers. Surrounding the box plot is the estimated kernel density that shows the shape of the data. In places with a wide distribution the probability to find data points is much larger compared to the narrower sections (figure 9.4).

Figure 9.4: A Violin Plot visualization based on data from [MacParland et al., 2018] showing a specific feature across different groups of cells. Note the options in the side panel.

A number of options exist when looking at the Violin Plot Side Panel.

Violin plot

Choose features, groups or a mixture to be displayed. Select how the data should be represented: as raw counts, log(1+raw counts) or normalized.

Display settings

Add a legend and values to the plot. Show and hide the box plot.

Colors

Customize the colors of the individual plots.

A number of options for zooming and adjusting the size of the plot is provided. By clicking the small icons () or () it is possible to fix either the x-axis or y-axis when zooming.

Figure 9.5: A Violin Plot visualization based on data from [MacParland et al., 2018]. Here, selected features are shown for hepatocyets. The percentage of cells expressing each feature is shown on top of the violins.

Several features can be displayed for one group of cells, as shown in figure 9.5. This can be useful for identifying marker genes when the cell types are not known or they need to be confirmed.

It is possible to pick and choose violins from different Violin Plots and to show them together in one plot. In order to do this, click on a feature or group label and right-click to bookmark the violin (see figure 9.6). All bookmarked violins will then appear in the same plotting area when selecting the bookmark option from the "Display for:" drop-down menu in the Side Panel. An example of a mixture plot is shown in figure 9.7.

Figure 9.6: A Violin Plot visualization based on data from [MacParland et al., 2018] showing how to bookmark a violin.

Figure 9.7: A Violin Plot visualization based on data from [MacParland et al., 2018] showing bookmarked violins with a mixture of both features and cell groups.

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