Differentially Expressed Proteins and Genes

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

Figure 1. Set up the Filter groups task to exlcude Doublets and cells that are not classified

This produces a Filtered counts data node (Figure ?).

Figure 2. Filter groups output

Re-split the Matrix

For this tutorial, we will re-split the protein and gene expression data prior to performing differential analysis. This is useful if you want to perform differential analysis and downstream analysis separately for each feature type. The split data nodes will both retain cell classification information.

For your own analyses, re-splitting the data is optional. You could just as well continue with differential analysis with the merged data if you prefer.

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 ?)

Figure 3. It is possible to re-split the merged matrix once again

Differential Analysis and Visualization - Protein Data

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.

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 Cell type 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 as Activated B cells vs. Mature B cells.

Click Finish to run the statistical test (Figure ?)

Figure 4. Setting up a comparison for differentially expressed proteins

The GSA task produces a GSA data node.

Double-click the GSA data 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 ?)

Figure 5. GSA report for protein expression data

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

Click in the CD45RA_TotalSeqB row

This opens a dot plot in a new data viewer session, showing CD45A expression for cells in each of the classifications (Figure ?)

Figure 6. CD45RA dot plot for all cells

We can use the Configuration panel on the left to edit this plot.

Expand the Summary card
Switch on Violins
Switch on Overlay
Switch on Colored
Expand the Data card
Use the slider to increase the Jitter
Expand the Color card
Use the slider to decrease the Opacity (Figure ?)

Figure 7. Use the Configuration panel to configure the dot plot

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 GSA data node
Click Exploratory analysis in the toolbox
Click Hierarchical clustering/heat map
Check Samples at the top to cluster the cells in addition to features
Click Finish to run with other default settings
Double-click the Hierarchical clustering task node to open the heat map (Figure ?)