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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 Peak Count Matrix
  • Import Cell Annotations
  • Import Cell Clusters
  • Import Cell Clonotypes
  • Cell format in importers
• Data export
  • Export Cell Ranger HDF5 Expression Matrix
  • Export Loom Expression Matrix
  • Export MEX Expression Matrix
  • Export TXT Expression Matrix
  • Export Peak Count Matrix
• Prepare Reads
  • Annotate Reads with Cell and UMI
    • Setting the sample name
    • Interpreting the output of Annotate Reads with Cell and UMI
• 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
    • 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
• Cell Type Classification
  • Browse QIAGEN Cell Ontology
  • 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
• 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
• Velocity Analysis
  • Single Cell Velocity Analysis
    • Interpreting the output of Single Cell Velocity Analysis
    • The velocity estimation algorithm
  • Differential Velocity for Single Cell
  • Score Velocity Genes
    • Interpreting the output of Score Velocity Genes
  • Create Phase Portrait Plot
• Chromatin Accessibility
  • Single Cell ATAC-Seq Analysis
    • Interpreting 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
• Immune Repertoire
  • Single Cell TCR-Seq Analysis
    • The report output from Single Cell TCR-Seq Analysis
    • The Cell Clonotypes element
    • The clonotype identification algorithm
  • Filter Cell Clonotypes
  • Combine Cell Clonotypes
  • Compare Cell Clonotypes
    • Interpreting the output of Compare Cell Clonotypes
  • Convert Clonotypes to Cell Annotations
• 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
    • Interpreting the output of Create Heat Map for Cell Abundance
• Dimensionality reduction
  • UMAP for Single Cell
  • tSNE for Single Cell
• Utility tools
  • Combine Cell Annotations
  • Combine Cell Clusters
  • Convert Metadata to Cell Annotations
  • Add Information to Plot
  • Update to Common 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 (10xVDJ)
    • Output from Immune Repertoire Analysis from Reads (10xVDJ)
  • Immune Repertoire and Expression Analysis from Reads (10xVDJ)
    • Configuring the batch units for Immune Repertoire and Expression Analysis from Reads (10xVDJ)
    • Output from Immune Repertoire and Expression Analysis from Reads (10xVDJ)
• 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
• UMAP and tSNE plot 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
• 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 Peak Count Matrix

The following peak matrix formats can be imported into a Peak Count Matrix ().

• Cell Ranger HDF5
• MEX
• MEX archive

Options common to all importers

Figure 2.2: The Cell Ranger Peak Count importer. The General options are common to all the peak matrix importers.

The first step of all importers take the following options as shown in Figure 2.2:

• Gene track Positions in the imported data are matched with the provided track.

  Matching is used to:

  1. View the Peak Count Matrix as a Track. For more information on tracks, see https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Tracks.html.
  2. Identify nearby genes if these are not explicitly supplied.
• Cell format and Sample: How cells are identified. See Cell format in importers for more details.

Figure 2.3: Additional options common to all the peak matrix importers.

The second step of all importers take the following options as shown in Figure 2.3:

• Nearby genes Nearby genes are determined in one of two ways:
  1. By searching for nearby genes using the Gene track and an accompanying mRNA track
  2. By supplying nearby genes in a selected tab-separated file (.tsv).

  The file must consist of either:

  1. 6 columns: name of the chromosome prefixed with "chr" (e.g., "chr1"), start and end position of the peak, the name of the gene, distance and type of peak.
  2. 4 columns: name of the chromosome together with start and end positions of the peak (e.g., "chr1:123-456"), the name of the gene, distance and type of peak.

  The first line must be column headers.

  The distance is the number of base positions from the start or end of the peak to the start or end of the gene, whichever is closest. It is signed and will be negative if the peak is before the gene.

  The type of the peak can be either "promoter" or "distal". Other values are ignored.

  If there are multiple nearby genes per peak, they can either be on separate lines or be grouped on one line, with gene name, distance and peak types separated by semi-colon.

• Transcription factors If enabled, transcription factors will be imported from the selected tab-separated bed file. Each line consists of the name of the chromosome (e.g., "chr1"), start and end positions of the peak, and the name of the transcription factor.

  If not enabled, the peak matrix will not have transcription factors.

The data to be imported may either consist of peak data only or it may be a mixture of peaks and gene expressions, as is the case for 10x Multiome files. In the latter case, the gene expressions must be imported into a separate Expression Matrix (see Import Expression Matrix).

Details specific to the MEX importer

The MEX importer requires three files to be supplied:

• Barcodes file A file with the extension .tsv and tab-separated columns, with one row per barcode. It can optionally contain a header. The barcodes are read from the first column. Empty lines are ignored.

  Use the Cell format option to control how the barcodes should be interpreted - for example if it also includes information about the sample.

• Feature or peak file This should be one of:
  1. A feature file with extension .tsv and six tab-separated columns, with one row per feature or peak. These are relevant for mixtures of peaks and expressions, e.g. 10x Multiome. The columns are: identifier (e.g., "chr1:123-456"); name (same as identifier for peaks); type, e.g. "Gene Expression" or "Peaks"; chromosome (e.g., "chr1"); start and end position of the feature or peak. The file can optionally contain a header. Empty lines are ignored.
  2. A peak file with extension .bed and three tab-separated columns: the chromosome, start and end position.
• Matrix file A file containing the expression with the extension .mtx in the Matrix Market Exchange Coordinate Format.

See https://math.nist.gov/MatrixMarket/formats.html for details of the Matrix Market Exchange Coordinate Format.

Details specific to the MEX archive importer

The MEX archive importer is provided for convenience. It accepts a .zip, .tar or .tar.gz archive file containing the files required by the MEX importer. In order to uniquely identify each file, these must have a specific name:

• Barcodes file must be named barcodes.tsv
• Feature or peak file must either be named features.tsv or peaks.bed
• Matrix file must be named matrix.mtx

Figure 2.4: Options for nearby genes and transcription factors for the MEX archive importer

The nearby genes and/or transcription factors can be passed in as separate files as is common to all the importers.

Alternatively, they can be included in the archive file. Then that should be indicated with checkbox "Archive has peak annotations" respectively "Archive has peak-motif associations". The file names must match a specific pattern:

• Nearby genes file must end with peak_annotation.tsv
• Transcription factor file must end with peak_motif_mapping.bed

This can be relevant for importing an archive produced by the peak matrix exporter using compression (see Export Peak Count Matrix). It may also be necessary when passing multiple files to a batching or iterating workflow (see Chromatin Accessibility and Expression Analysis from Matrix).

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