...

• Set the Counts filter to Keep cells between 500 and 20000 (Figure 5)
  

...

• Click the 2D radio button for Plot style to switch to the 2D UMAP (Figure 20)
  
  

Classify from expression and clustering

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• 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.

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

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

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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 Cytotoxic T cells for the name
• Click Save

To classify the helper T - cells, we can modify the selection criteria. 

...

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

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

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

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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
• Click Image Added twice to close the second and third genes
• Type CD19 in the search box
• Click CD19_TotalSeqB in the drop-down

...

• Click  to activate the lasso tool
• Draw a lasso around the CD19 protein-expressing clusters to select them
• Click  to filter to include only the selected cells
• Click  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 panel 
• Click the check box for 7 to select cluster 7
• Click Classify selection (Figure 34 )
  
  

...

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• Name the cells Doublets 
• Click Save 
• Click Clear selection 
• Click Image Added to filter to exclude the selected cells

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 expressed by plasma activated B cells. We can color the plot by both of these genes to visualize their expression.

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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.

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

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

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

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This will produce a Classified groups data node. 

Filter groups

Because we have classified our cells, we can now filter based on those classifications. This can be used to focus on a single cell type for re-clustering and sub-classification or to exclude cells that are not of interest for downstream analysis.

• Click the Classified groups data node
• Click Filtering 
• Click Filter groups
• Set to exclude Classifications is Doublets using the drop-down menus
• Click AND
• Set the second filter to exclude Classifications is N/A using the drop-down menus 
• Click Finish to apply the filter (Figure )
  
  

This produces a Filtered groups data node (Figure ).

Re-split the matrix

Prior to performing differential analysis, you may want to separate your protein and gene expression data. The split data nodes will both retain cluster and classification information.

• Click the Classified groups data node
• Click Pre-analysis tools
• Click Split matrix

This will produce two data nodes, one for each data type (Figure ).

Differential analysis and visualization

Once we have classified our cells, we can use this information to perform comparisons between cell types or between experimental groups for a cell type. In this project, we only have a single sample, so we will compare cell types. 

Protein expression

• Click the Antibody Capture data node
• Click Differential analysis 
  
• Click GSA

The first step is to choose which attributes we want to consider in the statistical test. 

• Check Classifications to include it in the statistical test
• Click Next 

Next, we will set up the comparison we want to make. Here, we will compare the Activated and Mature B cells.

• Check Activated B cells in the top panel
• Check Mature B cells in the bottom panel 
• Click Add comparison

The comparison should appear in the table.

• Click Finish to run the statistical test (Figure )
  
  

The GSA task produces a Feature list data node.

• Double-click the GSA task node to open the task report

The report lists each feature tested, giving p-value, false discovery rate adjusted p-value (FDR step up), and fold change values for each comparison (Figure ).

In addition to the listed information, we can access dot and violin plots for each gene or protein from this table.

Click 

Image Added

 in the 

CD25_TotalSeqB 

row

This opens a violin plot showing CD25 expression for cells in each of the classifications (Figure ).

Please see the Dot Plot documentation page to learn more about this visualization.

• Click the project name to return to the Analyses tab

To visualize all of the proteins at the same time, we can make a hierarchical clustering heat map.

• Click the Feature list data node
• Click Exploratory analysis in the toolbox
• Click Hierarchical clustering 
• Click Finish to run with default settings
• Double-click the Hierarchical clustering task node to open the heat map (Figure )
  
  

The heat map can easily be customized to illustrate our results.

• Click Image Added to transpose the heat map
• Set High to 2.8 to match the low range
• Set the Sample dendrogram to By sample attribute Classifications
• Set Attributes to Classifications
• Click Image Added and set Rotation to 0
• Uncheck Samples under Show labels

This generates a customized heat map to illustrate how the cell types differ in their protein expression (Figure ).

Gene expression

We can use a similar approach to analyze the gene expression data.

• Click the Gene Expression data node
• Click Differential analysis 
  
• Click GSA
• Check Classifications to include it in the statistical test
• Click Next 
• Check Activated B cells in the top panel
• Check Mature B cells in the bottom panel 
• Click Add comparison 
• Click Finish to run the statistical test

As before, this will generate a GSA task node and a Feature list data node.

• Double-click the GSA task node to open the task report (Figure )
  
  

Because 19,745 genes have been analyzed, it is useful to use a volcano plot to get an idea about the overall changes.

• Click Image Added to open a volcano plot

Each gene is shown as a point on the plot with cut-off lines for fold change and p-value or FDR step up set using the control panel on the left (Figure ). The number of genes up and down regulated according to the cut-offs is listed at the bottom of the plot. Mousing over a point shows the gene name and other information. 

• Click GSA report to return to the full report

We can filter the full set of genes to include only the significantly different genes using the filter panel on the left.

• Click FDR step up 
• Type 0.05 for the cutoff and press Enter on your keyboard or click the check box
• Click Fold change 
• Set to From -2 to 2 and press Enter on your keyboard or click the check box

The number at the top of the filter will update to show the number of included genes (Figure ).

• Click Generate filtered node to create a data node including only these significantly different genes

A task, Differential analysis filter, will run and generate a new Feature list data node. We can get a better idea about the biology underlying these gene expression changes using gene set or pathway enrichment. 

• Click the filtered Feature list data node
• Click Biological interpretation in the toolbox
• Click Pathway enrichment 
• Make sure that Homo sapiens is selected in the Species drop-down menu
• Click Finish to run
• Double-click the Pathway enrichment task node to open the task report

The pathway enrichment results list KEGG pathways, giving an enrichment score and p-value for each (Figure ).

• Click path:hsa04068 in the FoxO signaling pathway row

The KEGG pathway map shows up-regulated genes from the input list in red and down-regulated genes from the input list in green (Figure ).