This tutorial presents an outline of the basic series of steps for analyzing a 10x Genomics Gene Expression with Feature Barcoding (antibody) data set in Partek Flow starting with the output of Cell Ranger.  

...

The Analyses tab now includes a UMAP task node (Figure 18).

...

An advantage of UMAP over t-SNE is that is preserves more of the global structure of the data. This means that with UMAP, more similar clusters are closer together while dissimilar clusters are further apart. With t-SNE, the relative positions of clusters to each other are often uninformative.  

...

Classifying cells by clustering, gene, and protein expression

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

• Click  to filter to include only the selected cells
• Click  to rescale the axes to the included cells (Figure 22)
  
  

Because we merged the gene and protein expression data, we can visualize a mix of genes and proteins on the gene expression UMAP.

...

In addition to coloring by the expression of genes and proteins, we can select cells by their expression levels.

...

• Type CD3 in the ID search bar of the Features tab
• Click CD3_TotalSeqB from in the drop-down
• Click  to add a filter for CD3 protein expression

...

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

...

We can classify these cells. Because they express the pan T cell maker, CD3, and the cytotoxic marker, NKG7, but not the helper T cell marker, CD4, we can classify these cells as Cytotoxic T cells. 

...

• Click Classify selection
• Type Helper T cells for the name
• Click Save

...

• Click Clear selection
• Select Classification from the Color by drop-down menu (Figure 29)
  

To return to the full data set, we can clear the filter.

...

The zoom level will also be reset (Figure 30).

In addition to T-cells, we would expect to see B lymphocytes, at least some of which are malignant, in a MALT tumor sample. We can color the plot by expression of a B cell marker to locate these cells on the UMAP plot. 

• Choose Expression from the Color by drop-down menu
• Type CD19 in the search box
• Click CD19_TotalSeqB in the drop-down

 There are several clusters that show high levels of CD19 protein expression (Figure 31). We can filter to these cells to examine them more closely.

• Click Image Added to activate the lasso tool
• Draw a lasso around the CD19 protein-expressing clusters 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 (Figure 32)
  
  

We can use information from the graph-based clustering results to help us find sub-groups within the CD19 protein-expressing cells.

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

With the help of the Group biomarkers table, we can quickly characterize a few notable sub-groups based on their clusters (Figure 33).

Cluster 7, shown in pink, lists IL7R and CD3D, genes typically expressed by T cells, as two of its top biomarkers. Biomarkers are genes or proteins that are expressed highly in a clusters when compared with the other clusters. Therefore, the cells in cluster 7 are likely doublets as they express both B cell (CD19) and T cell (CD3D) markers. 

• Choose Graph-based from the Select by drop-down in the Attributes tab of the Selection / Filtering section of the control panel (Figure 34)
  
  

• Click the check box for 7 to select cluster 7
• Click Classify selection 
• Name the cells Doublets 
• Click Save 
• Click Clear selection 

The biomarkers for clusters 1 and 2 also show an interesting pattern. Cluster 1 lists IGHD as its top biomarker, while cluster 2 lists IGHA1. Both IGHD (Immunoglobulin Heavy Constant Delta) and IGHA1 (Immunoglobulin Heavy Constant Alpha 1) encode classes of the immunoglobulin heavy chain constant region. IGHD is part of IgD, which is expressed by mature B cells, and IGHA1 is part of IgA1, which is produced by plasma cells. We can color the plot by both of these genes to visualize their expression.

• Click IGHD in the Group biomarkers table
• Hold Ctrl on your keyboard and click IGHA1 in the Group biomarkers table

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

The clusters on the left show expression of IGHA1 while the larger or the two clusters on the right expresses IGHD. We can use the lasso tool to classify these populations.

• Select the left-hand cluster with IGHA1 expression (Figure 36)
  
  

• Click Classify selection
• Name them Plasma cells 
• Click Save
• Double-click any white-space on the plot to clear the selection

We can now classify the cluster that expresses IGHD as mature B cells. 

• Draw a lasso around the right-hand cluster (Figure 37)
  
  

• Click Classify selection
• Name them Mature B cells
• Click Save
• Double-click any white-space on the plot to clear the selection

We can visualize our classifications.

• Select Classifications from the Color by drop-down menu
• Click Clear filters to view all cells (Figure 38)
  
  

To use these classifications in downstream, we can apply the classifications.

• Click Apply classifications 
• Click Apply to confirm

This will produce a Classified groups data node.