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 ?)

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

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

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 ?).

• 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 ?)

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

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

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 ?).

• 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

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

The simplest way to classifying cell types is to look for the expression of key marker genes or proteins. This approach is more effective with CITE-Seq data than with gene expression data alone as the protein expression data has a better dynamic range and is less sparse. Additionally, many cell types have expected cell surface marker profiles established using other technologies such as flow cytometry or CyTOF. Let's compare the resolution power of the CD4 and CD8A gene expression markers compared to their protein counterparts.

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

• 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 ?). The protein expression data has a better dynamic range than the gene expression data, making it easier to identify sub-populations.

• On the first 2D scatter plot (with protein markers), click  in the top right corner
• Manually select the cells with high expression of the CD4_TotalSeqB protein marker (Figure ?)

More than 2000 cells show positive expression for the CD4 cell surface protein.

  

• Click  in the top right of the plot to switch back to pointer mode
• Click on a blank spot on the plot to clear the selection
• On the second 2D scatter plot (with mRNA markers), click  in the top right corner
• Manually select the cells with high expression of the CD4 gene marker (Figure ?)

This time, only 500 cells show positive expression for the CD4 marker gene. This means that the gene expression data is more sparse (i.e. there are more zeros), which further helps to reliably detect sub-populations.

T cells

Based on the exploratory analysis above, most of the CD3 positive cells are in the group of cells in the bottom right corner of the UMAP plot. This is likely to be a group of T cells. We will now examine this group in more detail to identify T cell sub-populations.

• Click  in the top right corner of both 2D scatter plots, to remove them from the canvas
• Click  in the top right corner of the 3D UMAP plot
• Draw a lasso around the group of putative T cells (Figure ?)

• Click  to include the selected points
• Click  in the top right of the plot to switch back to pointer mode
• Click and drag the plot to rotate it around

Deselected cells are excluded and the axes have been rescaled to give better resolution of the selected points (Figure ?). Note that the UMAP has not been recalculated, the axes have just been rescaled.

This group of putative T cells predominantly consists of cells assigned to graph-based clusters 3, 4, 6, and 7. Examining the biomarker table for these clusters can help us infer cell types.

• Click and drag the bar between the UMAP plot and the biomarker table to resize the biomarker table to see more of it (Figure ?)

If you need to create more space on the canvas, hide the selection panel using the icon on the right and/or the  icon to hide the menu on the left.

B cells

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