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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 Plain Text Table Expression Matrix
  • Export Peak Count Matrix
• Prepare Reads
  • Annotate Single Cell Reads
    • Read structure
    • Barcodes from names
    • Barcode correction
    • Sample name
    • 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
    • The output of QC for Single Cell
    • Choosing barcodes to retain
    • Cell calling
    • Automatic thresholds
    • Doublet calling
  • Demultiplex Parse Bio Samples
  • Normalize Single Cell Data
    • When is batch correction appropriate?
    • The output of Normalize Single Cell Data
    • The Normalize Single Cell Data algorithm
  • The Expression Matrix element
• Cell Type Classification
  • Browse QIAGEN Cell Ontology
  • Predict Cell Types
    • The output of Predict Cell Types
    • Cell type refinement
  • Train Cell Type Classifier
    • Features used for training and prediction
    • 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
    • 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
    • The output of Single Cell Velocity Analysis
    • The velocity estimation algorithm
  • Differential Velocity for Single Cell
  • Score Velocity Genes
    • The output of Score Velocity Genes
  • Create Phase Portrait Plot
  • Velocity analysis in workflows
  • The Velocity Matrix element
• Spatial Transcriptomics
  • The Spatial Transcriptomics Plot element
• Chromatin Accessibility
  • Single Cell ATAC-Seq Analysis
    • 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
  • The Peak Count Matrix element
• Immune Repertoire
  • Single Cell V(D)J-Seq Analysis
    • The report output from Single Cell V(D)J-Seq Analysis
    • The clonotype identification algorithm
  • Filter Cell Clonotypes
  • Combine Cell Clonotypes
  • Compare Cell Clonotypes
    • The output of Compare Cell Clonotypes
  • Convert Clonotypes to Cell Annotations
  • The Cell Clonotypes element
    • Primary and secondary clonotypes
    • Cell Clonotypes tables
    • Cell Clonotypes alignments
    • Cell Clonotypes Sankey plot
• 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
    • The output of Create Heat Map for Cell Abundance
  • Create Cell Annotations from Hashtags
  • The Cell Annotations element
  • The Cell Clusters element
• 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

Import Expression Matrix

Several formats can be imported into an Expression Matrix () using the following importers:

• AnnData: Import Expression Matrix in AnnData format ();
• Cell Ranger HDF5: Import Expression Matrix in Cell Ranger HDF5 format ();
• h5Seurat: Import Expression Matrix in h5Seurat format ();
• Loom: Import Expression Matrix in Loom format ();
• MEX: Import Expression Matrix in MEX format ();
• Parse Biosciences MTX: Import Expression Matrix in Parse Bio MTX format ();
• Plain Text Table: Import Expression Matrix in Plain Text Table format ().

The importers can be found here:

        Import () | Single Cell Data () | Import Expression Matrix ()

Some other commonly encountered formats are specific to a programming language or software package. These can usually be exported from that software package as Loom files. For example, .rds/.Robj formats are from the R programming language and can often be written to Loom using the LoomR package, or methods in the same R package that was used to generate the files.

General options

The following options are common to all expression matrix importers:

• Gene or Transcript track. Genes or transcripts in the imported data are matched with features in the provided track to the extent possible. When a match is found, the genomic coordinates of the gene/transcript will be recovered. Matches are only found when the identification of the gene/transcript in the imported data with the feature in the track is unambiguous: one-to-many and many-to-one matches between the imported data and the provided track are not supported. This means, for example, that if a gene is present on two chromosomes of the track, then neither set of genomic coordinates will be recovered.

  Matching is used to:

  • View the Expression Matrix as a Track. For more information on tracks, see https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Tracks.html.
  • Define the mitochondrial chromosome when calculating the proportion of reads mapped to mitochondria in the QC for Single Cell tool (Count-based and extra-chromosomal filters).
  • Recover identifiers (e.g. ENSG00000243485 for ENSEMBL genes) when these are not present in the input data. As identifiers are often more specific than e.g. gene names, this can help when training Cell Type Classifiers using the Train Cell Type Classifier tool (Train Cell Type Classifier), and when predicting cell types using a Cell Type Classifier.
  Although it is generally beneficial to provide a Gene or Transcript track that matches many of the genes or transcripts in the imported data, it is still possible to analyze data in the CLC Single Cell Analysis Module even when no matches are found.

  The matching algorithm works by choosing an approach from the following list that maximizes the number of one-to-one matches between features in the provided track and features in the imported data:

  • Matching names from the track with identifiers from the imported data
  • Matching identifiers from the track with identifiers from the imported data
  • Matching names from the track with unversioned identifiers from the imported data. An unversioned identifier is obtained by removing anything from or after the first `.' in the identifier. For example, ENSG00000243485 is the unversioned identifier for ENSG00000243485.5.
  • Matching identifiers from the track with unversioned identifiers from the imported data
  • Matching names from the track with names from the imported data
  • Matching identifiers from the track with names from the imported data

  In the case of a tie, the first equally good approach from the above list is used. If no matches are found, check that the correct Gene or Transcript track has been supplied.

• Spike-in controls (Optional). Genes or transcripts in the imported data are also matched against the spike-in controls provided here. This is used when calculating the proportion of reads mapped to spike-in controls in the QC for Single Cell tool (Count-based and extra-chromosomal filters). It is also used to remove the spike-in controls from downstream analysis. For details on how to import spike-in controls, see https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Import_RNA_spike_in_controls.html.
• Cell format and Sample. How cells are identified. See Cell format in importers for more details. When a file contains multiple samples, it is recommended to extract the sample name from the cell name. This allows the QC for Single Cell to process each sample separately, enables coloring of cells by sample in the Dimensionality Reduction Plot, and may simplify configuration of batch correction.

Options for importing cell annotations and clusters

AnnData, h5Seurat, Loom, and Parse Bio MTX can contain metadata about cells, and this can be imported as Cell Annotations () or Cell Clusters (). These importers share the following options:

• Create clusters for. A comma-separated list of attributes to be imported as Cell Clusters. Any other cells metadata will be imported as Cell Annotations.
• Map clusters to QIAGEN Cell Ontology. When this is checked, clusters will be translated, if possible, to the QIAGEN Cell Ontology (see The QIAGEN Cell Ontology). The translation attempts to match each cluster with a QIAGEN cell type based on the name and known synonyms. For example, `alveolar epithelial cells' are also called `pneumocytes'. If this option is selected, the `alveolar epithelial cells' cluster, if present, will be named `pneumocytes'. This option can be useful when standardizing clusters from different sources. It is especially recommended if clusters will be used to extend a QIAGEN Cell Type Classifier using the Train Cell Type Classifier tool (Train Cell Type Classifier).

Options for importing spliced and unspliced counts

Loom and MEX formats can contain both the total expression, spliced, and unspliced counts. The importers can be configured with which type of data to import and produce either an Expression Matrix (), or an Expression Matrix with spliced and unspliced counts ().

• Import expressions. Enables import of total expression from the relevant file. This is needed when:
  • spliced/unspliced counts are not available;
  • the total expression of a gene cannot be obtained purely from the spliced and, if selected, unspliced counts. For example, the expression has been normalized.
• Import spliced/unspliced. Enables import of spliced and unspliced counts from the relevant file(s). If the file(s) do not contain spliced/unspliced counts, the import will fail with a relevant message.
• Include unspliced counts in total expression. By default, the total expression of a gene is obtained from the spliced counts. When this option is checked, the unspliced counts are also added to the total expression. This option is recommended for single nucleus RNA sequencing (snRNA-Seq), where data is usually analyzed by counting expression from both exons and introns [Bakken et al., 2018]. This option has no effect when both `Import expressions' and `Import spliced/unspliced' are checked, where the total expression is read directly from the file.

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Subsections

• HDF5 formats
• Other formats

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