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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 Single Cell RNA-Seq Analysis report

An example of an scRNA-Seq report is shown in figure 7.1.

Figure 7.1: Report of an RNA-Seq run.

The report is a collection of the sections described below, some sections included only based on the input provided when starting the tool. If a section is flagged with a pink highlight, it means that something has almost certainly gone wrong in the sample preparation or analysis. A warning message tailored to the highlighted section is added to the report to help troubleshoot the issue. The report can be exported in PDF or Excel format.

Selected input sequences

Information about the sequence reads provided as input, including the number of reads in each sample, as well as information about the reference sequences used and their lengths.

References

Information about the total number of genes and transcripts found in the reference:

• Transcripts per gene. A graph showing the number of transcripts per gene.
• Exons per transcript. A graph showing the number of exons per transcript.
• Length of transcripts. A graph showing the distribution of transcript lengths.

Spike-in quality control

• Spike-in plot. A plot shows the expression of each spike-in as a function of the known concentration of that spike-in (see figure 7.2 to see an optimal spike-in plot).

  
  Figure 7.2: Spike-in plot showing how the points fall close to the regression line at high concentration.

• Summary table. A table provides more details on the spike-in detection. Figure 7.3 shows a failed spike-in control, with a table where results that require attention are highlighted in pink.

  
  Figure 7.3: Summary table where less than optimal results are highlighted.

  Under the table, a warning message explains what the optimal value was, and offers some troubleshooting measures: When samples have poor correlation between known and measured spike-in concentrations, it indicates problems with the spike-in protocol, or a more serious problem with the sample. To troubleshoot, check that the correct spike-in file has been selected, and control the integrity of the sample RNA. Also, if fewer than 10000 reads mapped to spike-ins, check that the correct spike-in sequences are specified, and consider using more spike-in mix in future experiments.

Read quality control

This section includes:

• A strand specificity table that indicates the direction of the RNA fragment that generated the read. Strandedness can only be defined for reads that map to a gene or transcript. Of these reads, the number of "Reads with known strand" is used in determining the percentage of reads ignored due to being on the wrong strand, and the subsequent percentage of reads with the wrong strand. In a strand-specific protocol, almost all reads are generated from a specific orientation, but otherwise a mix of both orientations is expected.
  • A warning message will appear if over 90% of reads were mapped in the same orientation but the tool was run without using a strand specific setting ("Forward"/"Reverse").
  • If over 25% of the reads were filtered away due to the strand specific setting, try to re-run the tool with strand specific setting "Both". However, if a strand-specific protocol was used, library preparation may have failed.
• A percentage of mapped paired-end reads containing read-through adapters. If present in above 10% of the reads, adapters may lead to false positive variant calls or incorrect transcript quantification (because reads must align within transcript annotations to be counted towards expression). Read-through adapters can be removed using the Trim Reads tool. In future experiments, consider selecting fragments that are longer than the read size. Note that single base extensions such as TA overhangs will also be classed as read-through adapters, and in these cases the additional base should be trimmed. Note also that trimming of read starts (5' trim) can lead to spurious detection of read-through adapters, because the trimming increases the number of read pairs where the end of one read aligns over the (trimmed) start of the other.
• A paired distance graph (only included if paired reads are used) shows the distribution of paired-end distances, which is equivalent to the distribution of sequenced RNA fragment sizes. There should be a single broad peak at the target fragment size. An asymmetric peak may indicate problems in size selection.

Mapping statistics

Shows statistics on:

• Paired reads or Single reads. The table included depends on the reads used. The table shows the number of reads mapped or unmapped, and in the case of paired reads, how many reads mapped in pairs and in broken pairs.

  If over 50% of the reads did not map, and the correct reference genome was selected, this indicates a serious problem with the sample. To troubleshoot, the report offers the following options:

  • Check that the correct reference genome and any relevant gene/mRNA tracks have been provided.
  • The mapping parameters may be too strict. Try resetting them to the default values.
  • Try mapping the unmapped reads against possible contaminants. If the sample is contaminated, enrich for the target species before library preparation in future experiments.
  • Library preparation may have failed. Check the quality of the sample RNA.

  In case paired reads are used and over 40% of them mapped as broken pairs, the report hints that there could be problems with the tool settings, a low quality reference sequence, or incomplete gene/mRNA annotations. It could also indicate a more serious problem with the sample. To troubleshoot, it is suggested to:

  • Check that the correct reference genome and any relevant gene/mRNA tracks have been provided.
  • Try re-running the tool with the "Auto-detect paired distances" option selected.
  • Check that the paired-end distances on the reads are set correctly. These are shown in the "Element Information" view on the reads. If these are correct, try re-running the tool without the "Auto-detect paired distances" option.
  • Try mapping the reads against possible contaminants. If the sample is contaminated, enrich for the target species before library preparation in future experiments.

• Match specificity. Shows a graph of the number of match positions for the reads. Most reads will be mapped 0 or 1 time, but there will also be reads matching more than once in the reference.

Fragment statistics

• Fragment counting. Lists the total number of fragments used for calculating expression, divided into uniquely and non-specifically mapped reads, as well as uncounted fragments (see the point below on match specificity for details).

• UMI fragment counting. Lists the total number of distinct UMI fragments used for calculating expression. This table is only included if the Library type setting is 3' sequencing and if the input reads are single end reads annotated with UMIs by tools of the Biomedical Genomics Analysis plugin.

• Counted fragments by type. Divides the fragments that are counted into different types, e.g., uniquely mapped, non-specifically mapped, mapped. A last column gives the percentage of fragments mapped for a particular type.
  • Total gene reads. All reads that map to the gene.
  • - Intron. From the total gene reads, reads that fall partly or entirely within an intron.
  • - Exon. From the total gene reads, reads that fall entirely within an exon or in an exon-exon junction.
  • - - Exon. From the total gene - exon reads, reads that map completely within an exon
  • - - Exon-exon. From the total gene - exon reads, reads that map across an exon junction .
  • Intergenic. All reads that map partly or entirely between genes.
  • Total. Total amount of reads for a particular type.

• Counted UMI fragments by type. Divides the distinct UMI fragments that are counted into different types, e.g., uniquely mapped, non-specifically mapped, mapped. The table contains the same rows as the 'Counted fragments by type' table (see above). It is only included in the report if the Library type setting is 3' sequencing and if the input reads are single end reads annotated with UMIs by tools of the Biomedical Genomics Analysis plugin.

Distribution of biotypes

Table generated from biotype annotations present on the input gene or mRNA tracks. If using both gene and mRNA tracks, the biotypes in the report are taken from the mRNA track.

• For genes, biotypes can be any of the following columns: "gene_biotype", "biotype", "gbkey", "type". The first one in this list is chosen.
• For transcripts, biotypes can be any of the following columns: "transcript_biotype", "biotype", "gbkey", "type". The first one in this list is chosen.

The biotypes are "as a percentage of all transcripts" or "as a percentage of all genes". For a poly-A enrichment experiment, it is expected that the majority of reads correspond to protein-coding regions. For an rRNA depletion protocol, a variety of non-coding RNA regions may also be observed. The percentage of reads mapping to rRNA should usually be <15%.

If over 15% of the reads mapped to rRNA, it could be that the poly-A enrichment/rRNA depletion protocol failed. To troubleshoot the issues in future experiments, check for rRNA depletion prior to library preparation. Also, if an rRNA depletion kit was used, check that the kit matches the species being studied.

Gene/transcript length coverage

Plot showing the normalized coverage across a gene/transcript body for four different groupings of gene/transcript length (figure 7.4).

Figure 7.5: Gene/transcript length coverage plot for data with a 3' bias.

To generate this plot, every transcript is rescaled to have a length of 100. For every read that is assigned to a transcript, we get its start and end coordinates in this "transcript-length-normalized" coordinate system [0,100]. We then increment counters from the read start position to the read end position. After all the reads have been counted, the average 5' count is the average value of the counters at position 0,1,2...49. The average 3' count is the value at positions 51,52,53...100. The difference between average 3' and 5' normalized counts is the difference between these values as a percentage of the maximum number of counts seen at any position.

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