...

• Drag and drop Add from the left panel to the right panel
• Set to 0.0001
• Select Finish

Filter samples

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

• Select the green Normalized counts data node
• Select Filter samples from the Filtering section of the task menu
• Set the filter to Include samples where Type is Oligodendroglioma 
• Select AND
• Set the second filter to Exclude samples where Classification is Microglia
• Select Finish to apply the filter

A Filtered Counts data node will be created with only cells that are from Oligodendroglioma samples and are classified as either malignant or oligodendrocyte. 

Identify differentially expressed genes

• Select the Normalized counts (green) the green Filtered counts data node
• Select Detect differential expression (GSA) 

The configuration options include cell-level attributes. Some of these are inherited from the sample level, like Type. Here, we want to compare only cells of a particular type from particular classification so we will include Type and Classification. 

• Select Type 
• Select Classification 
• Select Next

Next, we will set up a comparison between malignant cells and oligodendrocytes in Oligodendroglioma.

• Select Malignant and Oligodendroglioma in the top panels
• Select Oligodendrocytes in the bottom panels

Because we are analyzing sparse data, we need to relax the default low expression filter. By default the low expression filter is set a minimum of 1 average normalized read; here, we will turn it off.