Differential expression analysis can be used to compare cell types. Here , we will compare glioma and oligodendrocyte cells to identify genes differentially regulated in glioma cells from the oligodendroglioma subtype. Glioma cells in oligodendroglioma are thought to originate from oligodendrocytes, thus directly comparing the two cell types will identify genes that distinguish them. 

Filter cells

To analyze only the oligodendroglioma subtype, we can filter the samples.

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The filter lets us include or exclude samples based on sample ID and attribute. 

• Set the filter to Include samples where Subtype is Oligodendroglioma 
• Click AND
• Set the second filter to exclude Cell type (multi-sample) is Microglia 
• Click Finish to apply the filter (Figure 2)

A Filtered counts data node will be created with only cells that are from oligodendroglioma samples (Figure 3).

Identify differentially expressed genes

• Click the new Filtered counts data node
• Click Statistics > Differential analysis in the task menu
• Click GSA

The configuration options (Figure 4) includes sample and cell-level attributes. Here, we want to compare different cell types so we will include Cell type (multi-sample). 

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Next, we will set up a comparison between glioma and oligodendrocyte cells.

• Click Glioma in the top panel
• Click Oligodendrocytes in the bottom panel
• Click Add comparison (Figure 5)

This will set up fold calculations with glioma as the numerator and oligodendrocytes as the denominator. 

• Click Finish to run the GSA

A green GSA data node will be generated containing the results of the GSA. 

• Double-click the green GSA data node to open the GSA report

Because of the large number of cells and large differences between cell types, the p-values and FDR step up values are very low for highly significant genes. We can use the volcano plot to preview the effect of applying different significance thresholds.

• Click Image Removed to view the Volcano plot 
  
• Open the Style icon on the left, change Size point size to 6
• Open the Axes icon on the left and change the Y-axis to FDR step up (Glioma vs Oligodendrocytes)
• Open the Statistics icon and change the Significance of X threshold to -10 and 10 and the Y threshold to 0.001 
• Open the Select & Filter icon, set the Fold change thresholds to -10 and 10
• In Select & Filter, click Image Removed to remove the P-value (Glioma vs Oligodendrocytes) selection rule. From the drop-down list, add FDR step up (Glioma vs Oligodendrocytes) as a selection rule and set the maximum to 0.001

Note these changes in the icon settings and volcano plot below (Figure 6).

We can now recreate these conditions in the GSA report filter. 

• Click GSA report tab in your web browser to return to the GSA report
• Click FDR step up 
• Set the FDR step up filter to Less than or equal to 0.001
• Press Enter
• Click Fold change
• Set the Fold change filter to From -10 to 10
• Press Enter

The filter should include 291 genes. 

• Click Image Removed to apply the filter and generate a Filtered Feature list node

Exploring differentially expressed genes

To visualize the results, we can generate a hierarchical clustering heatmap. 

• Click the Filtered feature list produced by the Differential analysis filter task
• Click Exploratory analysis in the task menu
• Click Hierarchical clustering/heatmap

Using the hierarchical clustering options we can choose to include only cells from certain samples. We can also choose the order of cells on the heatmap instead of clustering. Here, we will include only glioma cells and order the samples by sample name (Figure 7).  

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• Double click the green Hierarchical clustering node to open the heatmap

The heatmap differences may be hard to distinguish at first; the range from red to blue with a white midpoint is set very wide because of a few outlier cells. We can adjust the range to make more subtle differences visible. We can also adjust the color. 

• Set the Range toggle Min to -1.5
• Set the Range toggle Max to 1.5

The heatmap now shows clear patterns of red and blue. 

• Click Axis titles and deselect the Row labels and Column labels of the panel to hide sample and feature names, respectively. 
• Select Sample name from the Annotations drop-down menu

Cells are now labeled with their sample name. Interestingly, samples show characteristic patterns of expression for these genes (Figure 8).

• Click Glioma (multi-sample) to return to the Analyses tab.

We can use gene set enrichment to further characterize the differences between glioma and oligodendrocyte cells. 

• Click the Filtered feature list node
• Click Biological interpretation in the task menu
• Click Gene set enrichment
• Change Database to Gene set database and click Finish to continue with the most recent gene set (Figure 9)

A Gene set enrichment node will be added to the pipeline .

• Double-click the Gene set enrichment task node to open the task report

Top GO terms in the enrichment report include "ensheathment of neurons" and "axon ensheathment" (Figure 10), which corresponds well with the role of oligodendrocytes in creating the myelin sheath that supports and protect axons in the central nervous system. 

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 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 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). 

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 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). 

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

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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.

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Normalization

Normalize (transform) the cells to account for variability between cells.  

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

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• Click Use recommended  
• Click Finish

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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

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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 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

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• Double-click the UMAP result node

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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.  

• 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.

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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. 

• 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 Image AddedAdd button
• Name the cell attribute
• Click Finish

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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. 

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

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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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