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• Click the Merged counts data node
• Click Exploratory analysis in the toolbox
• Click Scatter plot
• Click Finish to run 
• Double-click the Scatter plot task node to open it
• Click 2D to switch to a 2D plot style (Figure 44)

Similar to the t-SNE or UMAP scatter plots, each point on the plot is a single cell. The axes are set to features (gene or protein) in the data set by default, but can be set to any attribute or feature. On this plot, we can see that CD3_TotalSeqB is on the x-axis and CD4_TotalSeqB is on the y-axis. We can use our selection and filtering tools to perform a basic classification of CD4 and CD8 T cells. 

• Click the Features tab in the Selection / Filtering section of the control panel
• Type CD3 in the ID search bar of the Features tab
• Click CD3_TotalSeqB in the drop-down (Figure 45)
  
  

• Click Image Added to add a filter for CD3 protein expression
• Set the CD3_TotalSeqB filter to <= 2 

This will select any cell with <= 2 normalized count for CD3 protein. Selected cells are shown in bold on the plot and, because we have CD3_TotalSeqB on one of our axes, the cutoff point chosen can be easily evaluated (Figure 46). 

The selected CD3+ cells are our T cells. We can filter to these cells prior to performing our classification of CD4 and CD8 T cells sub-types.

• Click Image Added to filter to include only the selected cells

Next, we can switch the x-axis to show CD8 protein expression so that we can perform our classification.

• Click the X axis text box in the Plot setup section of the control panel
• Click CD8a_TotalSeqB from the drop-down list (or type it and then select it if it is not visible)
• Click Image Added to rescale the axes to the included cells

The x-axis now shows CD8a protein expression (Figure 47).

We can now use a set of filters to select and classify the CD3+ CD4+ CD8- T cells.

• Type CD4 in the ID search bar of the Features tab
• Click CD4_TotalSeqB in the drop-down
• Click Image Added to add a filter for CD4 protein expression
• Set the CD4_TotalSeqB filter to <= 2 
• Type CD8a in the ID search bar of the Features tab
• Click CD8a_TotalSeqB in the drop-down
• Click Image Added to add a filter for CD8a protein expression
• Set the CD8a_TotalSeqB filter to < 2 

This will select the cells in the upper left-hand section of the plot (Figure 48). 

• Click Classify selection 
• Name the group CD4 T cells
• Click Save

We can now select and classify CD3+ CD4- CD8+ T cells using the filters we have already created.

• Change CD4_TotalSeqB filter to < 1.5
• Change CD8a_TotalSeqB filter to >= 2

This selects the cells in the lower right-hand section of the plot (Figure 49). 

• Click Classify selection 
• Name the group CD8 T cells
• Click Save

To view our classifications, we can clear the selection and color by classification.

• Click Clear selection 
• Choose Classifications from the Color by drop-down menu (Figure 50).
  
  

An alternative approach to using the expression threshold filters is to draw a lasso around the population of interest using the lasso tool Image Added and then classify the selected cells.