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We will now examine the results of our exploratory analysis and use a combination of techniques to classify different subsets of T and B cells in the MALT sample.

Exploratory Analysis Results

Double click the UMAP data node
In the Configuration card on the left, expand the Color card and color the cells by the Graph-based attribute (Figure ?)

Flow Documentation > Classifying Cells > Color_UMAP.png

The 3D UMAP plot opens in a new data viewer session (Figure ?). Each point is a different cell and they are clustered together in the 3D plot based on how similar their expression profiles are across proteins and genes. Because a graph-based clustering task was performed upstream, a biomarker table is also displayed under the plot. This table lists the proteins and genes that are most highly expressed in each graph-based cluster. The graph-based clustering found 11 clusters, so there are 11 columns in the biomarker table.

Click and drag the 2D scatter plot icon from the Available plots card onto the canvas (Figure ?)
Drop the 2D scatter plot to the right of the UMAP plot

Flow Documentation > Classifying Cells > Add_2D_scatter_plot.png

Click Merged counts to use as data for the 2D scatter plot (Figure ?)

Flow Documentation > Classifying Cells > Merged_counts_for_2D_scatter_plot.png

A 2D scatter plot has been added to the right of the UMAP plot. The points in the 2D scatter plot are the same cells as in the UMAP, but they are positioned along the x- and y-axes according to their expression level for two protein markers: CD3_TotalSeqB and CD4_TotalSeqB, respectively (Figure ?).

Flow Documentation > Classifying Cells > UMAP_and_2D_scatter_plot.png

In the Selection card on the right, click Rule to change the selection mode
Click the blue circle next to the Add rule drop-down menu (Figure ?)

Flow Documentation > Classifying Cells > Selection_rule_mode.png

Click Merged counts to change the data source for the drop-down list
Choose CD3_TotalSeqB from the drop-down list (Figure ?)

Flow Documentation > Classifying Cells > Selection_rule_CD3_TotalSeqB.png

Click and drag the slider on the CD3D_TotalSeqB selection rule to include the CD3 positive cells (Figure ?)

Flow Documentation > Classifying Cells > Select_CD3_positive_cells.png

As you move the slider up and down, the corresponding points on both plots will dynamically update. The cells with a high expression for the CD3 protein marker (a marker for T cells) are highlighted and the deselected points are dimmed (Figure ?).

Flow Documentation > Classifying Cells > CD3_positive_cells_selected.png

Click Merged counts in the Data card on the left
Click and drag CD8a_TotalSeqB onto the 2D scatter plot (Figure ?)
Drop CD8_TotalSeqB onto the x-axis option

Flow Documentation > Classifying Cells > CD8_x_axis.png

The CD3 positive cells are still selected, but now you can see how they separate into CD4 and CD8 positive populations (Figure ?).

Flow Documentation > Classifying Cells > CD4_vs_CD8_scatter_plot.png

Let's compare the resolution power of the corresponding CD4 and CD8A gene expression markers.

Click the duplicate plot icon above the 2D scatter plot (Figure ?)

Flow Documentation > Classifying Cells > Duplicate_plot.png

Click Merged counts in the Data card on the left
Search for the CD4 gene
Click and drag CD4 onto the duplicated 2D scatter plot
Drop the CD4 gene onto the y-axis option
Search for the CD8A gene
Click and drag CD8A onto the duplicated 2D scatter plot
Drop the CD8A gene onto the x-axis option

The second 2D scatter plot has the CD8A and CD4 mRNA markers on the x- and y-axis, respectively (Figure ?).

Flow Documentation > Classifying Cells > Duplicated_plot_mRNA.png

On the second 2D scatter plot, click in the top right corner
Manually select the cells with high expression of the CD4 gene marker (Figure ?)