Use Principle Components Analysis (PCA) to reduce dimensions

• Click the Normalized counts data node 
• Expand the Exploratory analysis section of the task menu
• Click PCA 

In this tutorial we will modify the PCA task parameters, to not split by sample, to keep the cells from both samples on the PCA output. 

• Uncheck (de-select) the Split by sample checkbox under Grouping
• Click Finish

• Double-click the circular PCA node to view the results

From this PCA node, further exploratory tasks can be performed (e.g. t-SNE, UMAP, and Graph-based clustering).

Classify cells based on a marker for expression

• Choose Style under Configure 
• Color by and search for fasn by typing the name
• Select FASN from the drop-down

The colors can be customized by selecting the color palette then using the color drop-downs as shown below. 

Ensure the colors are distinguishable such as in the image above using a blue and green scale for Maximum and Minimum, respectively. 

• Click FASN in the legend to make it draggable (pale green background) and continue to drag and drop FASN to Add criteria within the Select & Filter Tool
• Hover over the slider to see the distribution of FASN expression

Multiple gene thresholds can be used in this type of classification by performing this step with multiple markers. 

• Drag the slider to select the population of cells expressing high FASN (the cutoff here is 10 or the middle of the distribution). 

• Click Classify under Tools
• Click Classify selection

• Give the classification a name "FASN high"

• Under the Select & Filter tool, choose Filter to exclude the selected cells

Exit all Tools and Configure options

• Click the "X" in the right corner 
• Use the rectangle selection mode on the PCA to select all of the points on the image 

This results in 147538 cells selected. 

• Open Classify 
• Click Classify selection and name this population of cells "FASN low" 
• Click Apply classifications and give the classification a name "FASN expression"

Now we will be able to use this classification in downstream applications (e.g. differential analysis).