Classify subpopulations & differential expression analysis
Last updated
Last updated
Astrocytes populations are often found in two activation states: the neurotoxic or pro-inflammatory phenotype (A1) and the neuroprotective or anti-inflammatory phenotype (A2). Now that we have identified our astrocyte population we can move onto the sub-classification of A1/A2 astrocytes. For this we are going to use the GFAP marker, a commonly used marker for A1 astrocytes.
Double click the Spatial report node in the Analyses tab of your project to open a new data viewer session.
Click Select & Filter , select Criteria, and add the 'Cell type' attribute in the criteria box.
Select only the Astrocytes and click to include only the selected points
Now add GFAP to the criteria box and toggle the Pin histogram option
The histogram shows the presence of two distinct populations that segregate based on GFAP expression
Set the upper threshold as 5
Classify the population
Classify > Classify selection, name the cells 'A2' and Save
Now slide the upper threshold to the max and set the lower threshold to 5, then Classify > Classify selection, name the cells 'A1' and Save
We are now ready to Apply classifications, name the attribute 'Astrocyte sub-population' and Run
Having classified our sub-populations we can now use that information to identify genes and biologically processes differentially activated between the two.
Click on the Normalized counts node, select Statistics > Differential analysis > Hurdle model, click Next
Select the Astrocyte sub-population > Add factors, then click Next
Drag A1 to the Numerator and A2 to the Denominator box, Add comparison, then click Finish
Once the differential expression analysis task has completed, you can explore the report and the subsequent analysis steps following this tutorial: Compare expression between cell types with multiple samples
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