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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 TXT Expression Matrix
  • Export Peak Count Matrix
• Prepare Reads
  • Annotate Single Cell Reads
    • Setting the sample name
    • Interpreting 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
    • 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
    • Interpreting the output of Predict Cell Types
    • Cell type refinement
  • Train Cell Type Classifier
    • Features used for training and prediction
    • Interpreting 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
    • 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
  • Velocity analysis in workflows
• Spatial Transcriptomics
  • The Spatial Transcriptomics Plot element
• 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 V(D)J-Seq Analysis
    • The report output from Single Cell V(D)J-Seq Analysis
    • The clonotype identification algorithm
  • The Cell Clonotypes element
    • Primary and secondary clonotypes
    • Cell Clonotypes tables
    • Cell Clonotypes alignments
    • Cell Clonotypes Sankey plot
  • 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
  • Create Cell Annotations from Hashtags
• 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

HDF5 formats

AnnData, Cell Ranger HDF5, h5Seurat and Loom are HDF5 formats, with specific requirements regarding structure of the data. An HDF5 file is organized in a hierarchical structure with:

• groups, containing zero or more groups or datasets;
• datasets: multidimensional arrays of data elements.

Metadata for groups and datasets is stored in associated attribute lists. Groups and datasets can often be themselves semantically interpreted as attributes.

All HDF5 importers contain an Expression matrix option, used for specifying the HDF5 file to be imported.

AnnData importer

The expression matrix in an AnnData (h5ad) is in a sparse dataset `X', while features and cells are described using the `var' and `obs' groups, respectively. See https://anndata.readthedocs.io/ for more details.

The `_index' attribute on group `obs' defines the cell identification, and the interpretation of this is specified by the Cell format.

• Gene or transcript ID attribute (Optional). A `var' attribute describing an identifier for a gene or transcript (e.g., ENSG00000243485 for ENSEMBL).
• Gene or transcript name attribute (Optional). A `var' attribute describing the name for a gene or transcript. If left empty, the `_index' attribute on group `var' is used.

h5Seurat importer

A h5seurat file may contain multiple assays and each assay may contain multiple expression matrices, e.g., counts and normalized expressions. The matrices can be sparse or dense. See https://mojaveazure.github.io/seurat-disk/articles/h5Seurat-spec.html for more details.

Only one assay and matrix can be imported at a time. The h5Seurat importer expects the format version 4.0.0.

The `cell.names' attribute contains the cell identification, and the interpretation of this is specified by the Cell format. If the sample is not set through Cell format or Sample, the sample for each cell is read from the `orig.ident' attribute on group `meta.data'.

The gene or transcript names are read from the `features' attribute of the selected assay.

• Assay (Optional). The name of the assay to import. If left empty, the assay in the `active.assay' attribute will be used.
• Import expressions from (Optional). The matrix for the selected assay to import. The matrix may be sparse (e.g., `counts' or `data') or dense (e.g., `scale.data'). If left empty, the importer will use `counts'.

Loom importer

A Loom file has an internal structure consisting of a main matrix, optional `layers' of the same size as the main matrix and row and column attributes (describing features and cells, respectively). See https://linnarssonlab.org/loompy/format/index.html for details on the format.

The Loom importer expects the Loom format version 3.0.0.

• Spliced layer. The layer where the spliced counts are stored.
• Unspliced layer. The layer where the unspliced counts are stored.
• Cell ID attribute (Optional). A column attribute identifying the cell by its barcode and sample. The interpretation of this value is specified by the Cell format.
• Gene or transcript ID attribute (Optional). A row attribute describing an identifier for a gene or transcript (e.g., ENSG00000243485 for ENSEMBL).
• Gene or transcript name attribute. A row attribute describing the name for a gene or transcript. If no names are present, then it is also possible to set this to the same value as the Gene or transcript ID attribute.

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