Here we are starting with Spacer Ranger outputs as the Single cell counts node.

Visium data analysis pipeline

A basic example of a spatial data analysis, starting from the Single cell counts node, is outlined shown 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). 

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Note that Single cell QA QA/QC has not been performed in this example, to visualize all spots (points) on the tissue image but would . Single cell QA/QC can 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). 

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Performing tasks in the Analyses tab

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Low-quality cells can be filtered out during the spatial data analysis using QA/QC 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). 

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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 (transform) the cells by accounting to account for variability between cells.  

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• Click the Normalized counts result node
• Select the PCA task under Exploratory analysis in the toolbox
• Unselect Split by Sample
• Click Finish

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The PCA result node generated by the PCA task can be visualized by double-clicking the circular node. 

• Single click the PCA result node
• Select the Graph-based clustering task  task from the toolbox
• Click Finish

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The results of graph-based clustering can be viewed by PCA, UMAP, or t-SNE. Follow the steps outlined below to generate a UMAP. 

• Select the Graph-based clustering result node by single click
• Select the UMAP task from the toolbox
• Click Finish

• Double-click the UMAP result node

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We can use these Cell attributes in analyses tasks such as Statistics (e.g. differential analysis comparisons) as well as to Style the visualizations in the Data Viewer. 

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