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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

Single Cell ATAC-Seq Analysis

Single Cell ATAC-Seq Analysis can be found in the Toolbox here:

        Toolbox | Single Cell Analysis () | Chromatin Accessibility () | Single Cell ATAC-Seq Analysis ()

The tool takes as input a single read mapping () of reads that have been annotated using Annotate Single Cell Reads. The tool outputs:

• A Peak Count Matrix () with annotated nearby genes and transcription factors.
• The Read Mapping () that was used for peak calling.
• An Annotation Track () of transcription factor motifs found within the peaks.
• A Graph Track () showing the footprint score at each position.
• A Report () providing a summary of the data and diagnostic plots for quality control.

It is important that the input read mapping contains all the samples that will be used in a downstream analysis. This is because it is not possible to combine Peak Count Matrices as they will typically have different coordinates for shared peaks. There are two ways to generate a single read mapping from multiple samples:

1. Provide multiple read lists to Map Reads to Reference http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Map_Reads_Reference.html.
2. Merge existing read mappings using Merge Read Mappings http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Merge_Read_Mappings.html.

The tool requires a Peak Shape Filter () for calling scATAC-Seq peaks, and both a Gene track () and a corresponding mRNA track () for assigning nearby genes to peaks. These data can be directly downloaded using the Reference Data Manager (see Reference data management).

It is also possible to supply custom Peak Shape Filter, Gene track and mRNA track as follows:

• Peak Shape Filters can be generated by Learn Peak Shape Filter (see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Learn_Peak_Shape_Filter.html).
• Gene and mRNA tracks can be imported from gff/gff3/gtf files (see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Import_tracks.html).

The following additional options are available:

• Maximum P-value for peak calling. The threshold for reporting peaks, higher values will increase the number of called peaks.
• Minimum peak count. The number of peaks a barcode must have to be called as a cell. Barcodes that do not have this many peaks will not be present in the Peak Count Matrix. This option is the scATAC-Seq equivalent of QC for Single Cell. It is effective despite its simplicity because:
  1. Peaks must be shared by other cells to have been detected by the peak caller, meaning that this metric is not affected by the presence of large numbers of randomly mapping reads.
  2. The minimum number of peaks is related to the amount of open chromatin per cell, which is presumed to have a high lower bound for any active cell.
  3. Sequencing is expected to sample peaks uniformly, so identifying non-cells is easier than for gene expression, where a cell might have so much expression of one gene that it is hard to detect others even though they are present.
• Chromosomes to ignore. As it lacks chromatin, many reads map to the mitochondria chromosome. Ignoring the mitochondria chromosome can therefore speed up analysis and improve results by removing the possibility that peaks are called there. If viral genomes have been added to the reference as decoys, then these should also be ignored. When configuring this option in a workflow, multiple chromosome names can be provided as a comma-separated list.

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Subsections

• 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

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