Perform Exploratory analysis

• Use Principle Components Analysis (PCA) to reduce dimensions

• Classify cells based on a marker for expression

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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