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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

The concept of CLC Single Cell Analysis Module

CLC Single Cell Analysis Module 24.1.1 enables the study of single cell RNA (scRNA-Seq) data, including RNA velocity, spatial transcriptomics, Assay for Transposase-Accessible Chromatin (scATAC-Seq) samples, and T and B cell receptors (scTCR-Seq and scBCR-Seq, collectively known as scV(D)J-Seq). The comprehensive toolbox includes tools for analyzing the different types of data both separately and jointly.

scRNA-Seq

Tools are available for quality control and normalization, noise reduction and feature selection, clustering, cell type prediction, and differential expression. UMAP and tSNE plots can be overlaid with clusters, predicted cell types, or the expression of individual genes. Marker genes can be identified through analyses of differential gene expression and by gathering information from expression plots. Alternatively, cell type classifiers can be trained from pre-labeled cells. Two pre-trained classifiers are provided, and can be extended. Further, velocity analysis can be performed for data including both spliced and un-spliced reads, and an interactive phase portrait plot is produced. In addition, high-dimensional vector that predicts the future state of individual cells are added to the dimensionality reduction plots. The provided workflows can be easily adjusted to fit the chemistry and protocol of the data and are a good starting point from either raw FASTQ or an imported Expression Matrix.

Spatial transcriptomics

A tool is available for importing spatial transcriptomics data from Space Ranger spatial outputs. The resulting plot can be overlaid with clusters, predicted cell types, or the expression of individual genes. Additionally, the plot can be linked to a UMAP or tSNE plot, such that the same visualization can be applied simultaneously to both plots.

scATAC-Seq

A complete pipeline from peak calling and footprinting to analysis of differential accessibility is provided. The peak read mappings can be slit into minor sub-populations and visualized in a Tracklist. It is also possible to generate UMAP and tSNE plots from the peak matrix. Further, three workflows are provided starting either from reads or imported matrices.

scV(D)J-Seq

After the initial identification of clonotypes in the sample, these can be further filtered, combined across samples and the sample-level immune repertoires can be compared with regards to diversity estimates, gene usage, etc. UMAP and tSNE plots from matched scRNA-Seq data can be overlaid with clonotype information, once this is converted to Cell Annotations. The provided workflows are a good starting point from either raw FASTQ or imported Cell Clonotypes. Additionally, workflows are available for the joint analysis of both scRNA-Seq and scV(D)J-Seq data.

Selection of algorithms

The algorithms implemented have been selected to be the best performing at the time of development as assessed by independent paper reviews. All algorithms have been re-implemented in Java with the aim of being able to scale to large data sets and run on a wide range of hardware. Internal benchmarks have been conducted to select the best performing algorithm for predicting cell types, which is one of the key features in this software package. The manual provides detailed descriptions of the chosen algorithms, how to adjust parameters for better performance, and how to interpret results.

The CLC Single Cell Analysis Module is frequently updated. A detailed list of new features, improvements, bug fixes, and changes is available at https://digitalinsights.qiagen.com/clc-single-cell-analysis-module-latest-improvements/.

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