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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 Peak Count 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
  • 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 Prediction
  • 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
    • 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 from Chromatin Accessibility and Expression Analysis
    • Importing matrices
  • 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
  • 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

Export Loom Expression Matrix

For export in Loom format a number of parameters can be specified, see figure 3.3.

Figure 3.3: Exposed parameters for export of expression matrix in Loom format

Matrix export parameters include the following options:

• Cell format can be specified to include both the sample and barcode. Hover the mouse over this setting to see the possible options. Often the barcode in itself is sufficient as a unique identifier of cells. However, when an expression matrix contains multiple samples, the cell format must also include the sample. NOTE: when a matrix contains more than one sample the exporter will fail if sample handling has not been defined properly.
• Clusters Cell clusters as defined in an accompanying Cell Clusters () element
• Cell annotation Cell annotations as defined in an accompanying Cell Annotations () element

In addition, it is optional to export normalized data.

Export normalized If selected and the matrix has been normalized with Normalize Single Cell Data (see Normalize Single Cell Data), the normalized expressions will be exported instead of the raw counts. If the matrix has not been normalized, the export will fail.

Use compression select among gzip, zip or no compression which is default.

Output file name is the name of the file and can be customized by changing the default pattern in Custom file name.

The Loom exporter creates a Loom file in format version 3.0.0, see https://linnarssonlab.org/loompy/format/index.html for details. In particular:

• The version number is written as an HDF5 dataset at /attrs/LOOM_SPEC_VERSION.
• The expression data, either raw counts or normalized, is written as an HDF5 dataset at /matrix.
• The cell ID, composed of sample and barcode, is written as an HDF5 group at col_attrs/CellID.
• The feature IDs and names are written as HDF5 groups at col_attrs/Accession and col_attrs/Gene, respectively.
• Any exported clusters and annotations are written as additional HDF5 groups at col_attrs.
• An empty layers HDF5 group is created.

To load a Loom file "myFile.loom" in Seurat, the following code may be useful:

library(Seurat)
library(loomR)
lfile <- connect(filename="myFile.loom", mode="r", skip.validate=TRUE)
mySeurat <- as.Seurat(lfile)
lfile$close()

To load a Loom file "myFile.loom" in Scanpy, the following code may be useful:

import scanpy as sc
adata = sc.read_loom('myFile.loom',validate=False)

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