Here we are starting with Spacer Ranger outputs as the Single cell counts node.
A basic example of a spatial data analysis, starting from the Single cell counts node, is 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).
Note that QA/QC has not been performed in this example, to visualize all spots (points) on the tissue image. 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).
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 using QA/QC and will not be viewed on the tissue image. 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).
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.
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
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 from the toolbox
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.
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
The UMAP is automatically colored by the graph-based clustering result in the previous node. To change the color, click Style.
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 the project to make this attribute accessible for downstream applications.
Click the Classify result node
Select Publish cell attributes to project under Annotation/Metadata
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).
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 comparisons) as well as to Style the visualizations in the Data Viewer.
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Select cell_type from the drop-down and click the green Add button
