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; directly comparing the two cell types will identify genes that distinguish them. 

Filter groups

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

• Click the green Classified groups data node
• Click Filtering in the task menu
• Click Filter groups (Figure 1)
  
  

Figure 1. Invoking the sample filter

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 Classifications 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 green Filtered counts data node
• Click Differential analysis in the task menu
• Click GSA (Figure 5)
  
  

• Click Classifications
• Click Next
  
  

Figure 5. Choosing attributes to include in the statistical test

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

• Click Glioma in the top panel
• Click Oligodendrocytes in the bottom panel
• Click Add comparison (Figure 7)

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

Figure 6. Defining the comparison between Glioma and Oligodendrocytes

• Click None in the Read count normalization section
• Click Finish to run the GSA

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

• Double-click the green Feature list 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.

• Click  to view the Volcano plot 
  
• Choose FDR step up from the Y axis source drop-down menu
• Set the X axis significance threshold to 10
• Set the Y axis significance threshold to 0.001

This gives 133 up-regulated and 158 down-regulated genes (Figure 8).

 

Figure 7. Previewing a filter by adjusting the significance thresholds

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

• Click GSA report at the top of the screen to return to the GSA report
• Click FDR step up 
• Set the FDR step up filter to Less than or equal to 0.001
• Click Fold change
• Set the Fold change filter to From -10 to 10

The filter should include 291 genes. 

• Click  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 heat map. 

• Click the green Feature list produced by the Filter list task
• Click Exploratory analysis in the task menu
• Click Hierarchical clustering 

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 heat map instead of clustering. Here, we will include only glioma cells and order the samples by sample name (Figure 9).  

• Uncheck Cluster samples 
• Click Filtering and set the filter to include Classifications is Glioma
• Choose Sample name from the Sample order drop-down menu in the Ordering section
• Click Finish 
  
  

Figure 8. Configuring hierarchical clustering

• Double click the green Hierarchical clustering node to open the heat map

The heat map will appear black at first; the range from red to green with a black midpoint is set very wide because of a few outlier cells. We can adjust the range to make more subtle differences visible. 

•  Set Low to -2
• Set High to 2

The heat map now shows clear patterns of red and green. 

• Click Samples and Features in the Show labels section of the panel to deselect them and hide sample and feature names
• Select Sample name from the Attributes drop-down menu

Cells are now labeled with their sample name. Interestingly, samples show characteristic patterns of expression for these genes (Figure 10).

Figure 9. Hierarchical clustering heat map with cells on rows (ordered by sample name) and genes on columns (clustered)

• Click Glioma (multi-sample) to return to the pipeline view

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

• Click the second green Feature list node
• Click Biological interpretation in the task menu
• Click Enrichment analysis (Figure 11)

Figure 10. Invoking Enrichment analysis

• Choose Homo sapiens (human) - hg38 from the Assembly drop-down menu
• Select Finish to continue with the most recent gene set

A GO enrichment node will be added to the pipeline view (Figure 12).

Figure 11. Completed tutorial analysis pipeline

• Double-click the green GO enrichment task node to open the task report

Top GO terms in the enrichment report include "myelin sheath", "ensheathment of neurons", and "axon ensheathment" (Figure 13), which corresponds well with the role of oligodendrocytes in creating the myelin sheath that supports and protect axons in the central nervous system. 

Figure 12. GO enrichment task report

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