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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

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.

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

Output file name is the name of the file and can be customised 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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