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Classify from expression and clustering

• Click  to activate the lasso tool
• Draw a lasso around clusters 3, 4, and 6 (Figure 21) to select them
  
  

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This will color the plot by IGHD and IGHA1 (Figure 35).

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This will produce a Classified groups data node. 

Clustering by protein expression

In addition to performing clustering by gene expression data, we can use the protein data for clustering and UMAP visualization. 

• Click the Classified groups data node
• Click Exploratory analysis in the toolbox
• Click Graph-based clustering 
• Click Antibody Capture for Include features where "Feature type" is
• Click Finish to run 

Notice that we did not set the number of PCs in this case. If there are fewer than 50 proteins in the data set, all possible PCs will be used by default and, because using all the PCs will capture all of the variance in the data set, this is equivalent to running clustering on the original data. If you data set has more than 50 proteins and you want to run clustering on full data instead of a subset of PCs, simply set the number of PCs to All in the advanced settings.

• Click the Clustering result data node 
• Click Exploratory analysis in the toolbox
• Click UMAP
• Click Antibody Capture for Include features where "Feature type" is
• Click Finish to run

We can open the UMAP task report to view the clustering result.

• Double-click the UMAP task node
• Click Group biomarkers to minimize the biomarkers table

UMAP using the protein expression data resolves the cell types we identified earlier on the gene expression UMAP (Figure 39).

We can take a closer look at the helper T cell cluster to see if any additional cell types can be found using the protein expression data.

• Click Image Added to activate the lasso tool
• Draw a lasso around the Helper T cell cluster to select them
• Click Image Added to filter to include only the selected cells
• Click Image Added to rescale the axes to the included cells 

With that, let's take a look at the clustering results from the protein expression data for these cells.

• Choose Graph-based from the Color by drop-down menu

Please note that Graph-based always refers to the most recent graph-based clustering result in the pipeline. 

• Click Group biomarkers to expand the biomarkers table
• Select Graph-based from the Method drop-down menu (Figure 40)

The far-left cluster, cluster 8, has several interesting biomarkers. The top biomarker, is CXCL13, a gene expressed by follicular B helper T cells (Tfh cells). Two of the other biomarkers are PD-1 protein, which promotes self-tolerance and is a target for immunotherapy drugs, and TIGIT, another immunotherapy drug target. 

• Choose Expression from the Color by drop-down menu
• Type PD-1 in the search box and choose PD-1_TotalSeqB from the drop-down

PD-1 expression is highest in cluster 8 with uniformly strong expression throughout (Figure 41).

• Type PDCD1 in the Expression search box and choose PDCD1 from the drop-down

It is interesting to note that this pattern of PD-1 expression is not easily discernible at the PD-1 gene expression level (PDCD1) (Figure 42). 

• Type CXCL13 in the Expression search box and choose CXCL13 from the drop-down

The Tfh cell marker, CXCL13, is highly and specifically expressed in cluster 8, so we will classify these cells as Tfh (Figure 43). 

• Choose Graph-based from the Select by drop-down in the Attributes tab of the Selection / Filtering section of the control panel 
• Click the check box for 8 to select cluster 8
• Click Classify selection 
• Name the cells Tfh cells
• Click Save 
• Choose Classifications from the Color by drop-down menu
• Click Clear selection 
• Click Clear filters to return to the full data set
• Click Apply classifications