Perform Exploratory analysis
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).
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