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. 

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. 

• Click the Filtering drop-down in the toolbox
• Click the Filter Features task 
• Choose Noise reduction
• Exclude features where value <= 0.0 in at least 99.0% of the cells 
• Click Finish

Remove gene expression values that are zero in the majority of the cells.

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 to account for variability between cells.  

• Select the Filtered Counts result node
• Choose the Normalization task from the toolbox 

• Click Use recommended  
• Click Finish

Exploratory analysis

Explore the data by dimension reduction and clustering methods. 

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

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 
• Click Finish

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

• Click the Graph-based clustering result node
• Select the UMAP task
• Click Finish

• Double-click the UMAP result node

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.  

• Click the Filtered counts node
• From the Classification drop-down in the toolbox, select Classify cell type 
• Using the Managed classifiers, select the human Intestine Garnett classifier
• Click Finish

The output of this task produces the Classify result node.

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. 

• Click the Classify result node
• Select Publish cell attributes to project under Annotation/Metadata 
• Select cell_type from the drop-down and click the green Add button
• Name the cell attribute
• Click Finish

Publish cell attributes can be applied to result nodes with cell annotation (e.g. click the graph-based clustering result node and follow the same steps). 

An example of this completed task is shown below. 

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). 

• Click Manage 
• Click the Action dots
• Choose Modify attribute 
• Rename the attribute Cell Type 
• Click Save
• Click Back to metadata tab

Drag and drop the categories to rearrange the order of these categories, The order here will determine the plotted order and legend in visualizations. 

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

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