Expression Analysis from Reads

The workflow Expression Analysis from Reads takes Reads as input and starts by annotating them with cell barcode and UMI, followed by trimming and mapping to create one or more Expression Matrix (Image expression_matrix_track_16_n_p) / (Image expr_matrix_spliced_unspliced_16_n_p). Then it performs quality control, normalization, clustering, cell type prediction and velocity analysis. The workflow uses iterate functionality and allows for a combined analysis of multiple samples to produce:

The workflow can be found in the Template Workflows section here:

        Single Cell Workflows (Image sc_workflow_folder_open_16_n_p) | From Reads (Image sc_wf_from_reads_folder_open_16_n_p) | Expression Analysis from Reads (Image sc_rna_from_reads_16_n_p)

If you are connected to a CLC Server via your Workbench, you will be asked where you would like to run the analysis. We recommend that you run the analysis on a CLC Server when possible.

You can choose either one or more Sequence lists or Select files for import and select FASTQ files for importing.

The workflow offers a number of options described below. Note that not all parameters can be configured. Open parameters indicate places where customization may be necessary for different samples, but default settings are suitable in most cases.

The workflow can be run using Single Cell hg38 (Ensembl) or Single Cell Mouse (Ensembl) reference data sets (see The Reference Data Manager).

Note: Reference data elements cannot be configured during workflow execution. If other elements than those provided in the default reference data sets are needed, a custom reference data set can be used, see  http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Custom_Sets.html. When creating custom reference data sets, the chosen gene track needs to match the gene annotations used for training the provided Cell Type Classifier (Image cell_type_classifier_16_n_p) (see Features used for training and prediction).

The workflow allows the analysis of multiple samples and you can specify metadata during workflow execution for configuring which inputs belong to which sample. When there is only one library per sample, metadata is not necessary and "Use organization of input data" can be used, but metadata can still be useful, as it is converted to cell annotations and can be used for coloring the cells in the Dimensionality Reduction Plot. For more details on configuring workflow execution with metadata, see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Running_workflows_in_batch_mode.html. Make sure to inspect the batch overview to check that the analysis will be performed correctly.

Examples for how to use metadata for workflow execution can be found in Configuring the batch units for Expression Analysis from Reads.

It is important to select the proper read structure for annotating the reads with cell barcode and UMI. If the data has not been prepared using one of the predefined protocols, a custom read structure can be specified as detailed in Annotate Reads with Cell and UMI, where a list of many different single cell protocols is also linked. However, this requires editing the workflow, see  http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Creating_editing_workflows.html for details.

Spike-in controls can be provided, if used during sample preparation. To learn how to import spike-in control files, see http://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Import_RNA_spike_in_controls.html.

The strand specificity and expected coverage bias must be specified. Strand specific "Forward" is most common, though 5' sequencing often requires strand specific "Reverse". For 5' sequencing, we recommend setting coverage bias to "Targeted". If an unsuitable strand specificity or coverage bias is chosen, warnings may be shown in the output RNA-Seq report (for details see The Single Cell RNA-Seq Analysisreport.)

An option to count intronic reads towards gene expression is also present. This is recommended when many transcripts are expected to be unprocessed, as is the case for single nucleus RNA sequencing.

For quality control a number of options exist. The option to remove empty droplets is not suitable for protocols that do not use droplets, and removing barcodes with low number of reads or expressed features might be more appropriate. Quality Control (QC) uses the number of reads mapped to the mitochondria, and for this the name of the mitochondria chromosome needs to be provided. The default value is often the correct name. After quality control, the matrices are collected and normalized jointly. Note that batch correction is not performed. Read more about QC and normalization in Gene Expression Matrix.

For clustering and creation of the Dimensionality Reduction Plot plot, it is possible to restrict analysis to highly variable genes. The data is then projected to a lower dimensional space using PCA. You can read about this feature in Feature selection and dimensionality reduction.

Velocity is calculated for each sample individually by default. If unticked, the calculated velocity is performed using all cells across samples.

The high confidence predicted cell types ("Cell type (high confidence)") are used to group the cells in the expression plots (Heat Map and Dot Plot) and Cell Abundance Heat Map, as well as for scoring the velocity genes. The Cell Abundance Heat Map additionally groups the cells based on the automated clusters obtained with resolution 1.0 ("Leiden (resolution=1.0)"). Any of these groups can be changed to:


Subsections