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Visium data analysis pipeline
A basic example of a spatial data analysis, starting from the Single cell counts node, is outlined below and is similar to a Single cell RNA-Seq analysis pipeline with the addition of the Spatial report task (shown) or Annotate Visium image task (not shown). Note that Single cell QA/QC has not been performed in this example to visualize all spots (points) on the tissue image but would be performed from the Single cell counts node with the filtered cells applied to the Single cell counts before the Filter features task. Click here for more information on Single cell QA/QC (see the pipeline in Figure 11).
Performing tasks in the Analyses tab
A context-sensitive menu will appear on the right side of the pipeline. Use the drop-downs in the toolbox to open available tasks for the selected data node.
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Low-quality cells can be filtered out during the spatial data analysis and will not be viewed on the tissue image. A few examples of low-quality cells are doublets, cells damaged during cell isolation, or cells with too few counts to be analyzed. Click here for more information on Single cell QA/QC. We will not perform Single cell QA/QC in this tutorial; this task would be invoked from the Single cell counts node and the Filter features task discussed below would be invoked from this output node (Filtered counts).
Filter Features
Remove gene expression counts that are not relevant to the analysis.
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A task node, Filtered counts, is produced. Initially, the node will be semi-transparent to indicate that it has been queued, but not completed. A progress bar will appear on the Filter features task node to indicate that the task is running.
Normalization
Normalize the cells by accounting for variability between cells.
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- Click Use recommended
- Click Finish
Exploratory analysis
Explore the data by dimension reduction and clustering methods.
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The UMAP is automatically colored by the graph-based clustering result in the previous node. To change the color, click Style.
Automatic classification
Classify the cells using Garnett automatic classification to determine cell types.
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Double-click the Classify result node to view the cell count for each cell type and the top marker features for each cell type.
Publish cell attributes to project
Publish cell attributes to the project to make this attribute accessible for downstream applications.
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Since this attribute has been published, we can choose to right-click the Publish cell attributes to project node and remove this from the pipeline. This attribute will be managed in the Metadata tab (discussed below).
Modify cell attribute
The name of the Cell attribute can be changed in the Metadata tab (right of the Analyses tab).
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