t-SNE (t-distributed stochastic neighbor embedding) is a visualization method commonly used analyze single-cell RNA-Seq data. Each cell is shown as a point on the plot and each cell is positioned so that it is close to cells with similar overall gene expression. When working with multiple samples, a t-SNE plot can be drawn for each sample or all samples can be combined into a single plot. Viewing samples individually is the default in Partek® Flow® because sample to sample variation and outlier samples can obscure cell type differences if all samples are plotted together. However, as you will see in this tutorial, in some data sets, cell type differences can be visualized even when samples are combined.

Using the t-SNE plot, cells can be classified based on clustering results or differences in gene and pathway expression. 

Multiple single-sample t-SNE plots

By default, each sample in a multi-sample data set is plotted on its own t-SNE. 

• Click the Filtered counts node
• Select t-SNE from the Exploratory analysis section of the task menu (Figure 1)
  
  

Figure 1. Invoking t-SNE from the task menu

• Click Finish from the t-SNE dialog to run t-SNE with the default settings

A t-SNE task node will be generated (Figure 2).

 

Figure 2. t-SNE task node

Once the t-SNE task has completed, we can view the t-SNE plot.

• Click the t-SNE node
• Click Task report from the task menu or double click the t-SNE node

The t-SNE plot will open to the first sample in the data set, MGH36 (Figure 3). Please note that the appearance of the t-SNE plot will differ each time it is drawn so your t-SNE plots will look different than those shown in this tutorial; however, the cell-to-cell relationships indicated will be the same. 

 

Figure 3. Viewing t-SNE plot of a single sample

The t-SNE plot is in 3D by default. You can rotate the 3D plot by left-clicking and dragging your mouse. You can zoom in and out using your mouse wheel. The 2D t-SNE is also calculated and you can switch between the 2D and 3D plots using the Plot style radio buttons. 

Each sample has its own plot. We can switch between samples using the Back and Next buttons on the upper left. 

• Select Next
  

The t-SNE plot has switched to show the next sample, MGH42 (Figure 4). 

 

Figure 4. Viewing t-SNE plot of MGH42

The goal of this analysis is to compare malignant cells from two different glioma subtypes, astrocytoma and oligodendroglioma. To do this, we need to identify which cells are the malignant cells we want to include and which cells are the normal cells we want to exclude. 

The t-SNE plot in Partek Flow offers several options for identifying, selecting, and classifying cells. In this tutorial, we will use expression of known marker genes to identify cell types. 

To visualize expression of a marker gene, we can color cells on the t-SNE plot by their expression level. 

• Open the Color by drop-down menu
• Select Gene expression from the drop-down menu (Figure 5)
  
  

Figure 5. Selecting color by gene expression

The cells will turn black and a text box Gene ID will open below the drop-down box. 

• Type BCAN in the Gene ID text box
• Select BCAN from the list of genes in the data set (Figure 6)
  
  

Figure 6. Coloring cells by BCAN expression

The cells will be colored from black to green based on their expression level of BCAN, with cells expressing higher levels more green (Figure 7). BCAN is highly expressed in glioma cells. 

 

Figure 7. Cells colored by CD14 expression

In Partek Flow, we can color cells by more than one gene. We will now add a second glioma marker gene, GPM6A. 

• Select the  icon next to BCAN
• Type GPM6A in the new Gene ID box
• Select GPM6A from the list of genes in the data set

Cells expressing GPM6A are now colored red and cells expressing BCAN are colored green. Cells expressing both genes are colored yellow, while cells expressing neither are colored black (Figure 8).

 

Figure 8. Coloring cells by BCAN and GPM6A

Relative expression of the two genes for selected cells can be visualized on the legend. 

• Activate the 3D lasso tool by selecting  
• Draw a circle around the cluster of yellow cells (Figure 9)
  
  

Figure 9. Selecting a group of cells using the 3D lasso tool

Selected cells are shown in bold and unselected cells are dimmed.

The relative expression of the two genes for the selected cells will be shown on the legend as dots (Figure 10). 

 

Figure 10. Viewing expression levels for a group of cells

Numerical expression levels for each gene can be viewed for individual cells. 

• Switch modes by clicking 
• Select a cell by pointing and clicking

The expression level for that cell is displayed on the legend for each gene (Figure 11). 

• Deselect the cell by clicking on any black space on the plot

Expression values can also be viewing by selecting Gene Expression from the Label by drop-down menu and mousing over a cell. 

 

Figure 11. Viewing expression levels for an individual cell. The dots on the legend indicate the expression level of the selected cell.

Now that cells are colored by expression of two glioma cell markers, we can classify any cell that expresses these genes as glioma cells. Because t-SNE groups cells that are similar across the high-dimensional gene expression data, we will consider cells that form a group with BCAN or GPM6A-expressing cells as same cell type, even if they do not express the marker gene.

• Click anywhere on the t-SNE plot without a cell to clear the selection

• Activate the 3D lasso tool by clicking 
• Draw the lasso around the cluster of yellow cells and click the circle to close the lasso. You may need to switch to selection mode and rotate the 3D plot to select only cells from the yellow cluster

The number of selected cells is indicated in the Selection section of the menu.

• Select Classify selection (Figure 12)
  
  

A dialog to give the classification a name will appear. 

• Name the classification Glioma
• Click Save (Figure 13)
  
  

Figure 13. Classifying selection

Once cells have been classified, the classification is added to the Classifications section of the panel. The number of cells belonging to the classification is listed; in MGH42, there are 462 glioma cells (Figure 14). 

 

Classifications made on the t-SNE plot are retained as a draft after you exit the t-SNE task report. The Apply classifications button runs a task, Classify cells, which generates a new Classified cells data node. In this tutorial, we will classify malignant cells for each sample before we save the classifications, but if necessary, you can exit the t-SNE task report and continue classifying the next sample later. 

• Click Next to move to the next sample, MGH45
• Rotate the 3D t-SNE plot to allow you to select only cells from the yellow cluster
• Activate the 3D lasso tool by selecting 
• Draw the lasso around the cluster of black cells and click the circle to close the lasso (Figure 14). 
  
  

Figure 15. Classifying malignant cells

• Select Classify selection 
• Type Glioma or select Glioma from the prompt (Figure 15)
• Click Save
  
  

Figure 16. Adding cells in a second sample to an existing classification

• Repeat these steps for each of the 6 remaining samples

Once all samples have been classified, it is useful to check the number of cells in each sample assigned to each classification. 

• Click Summary (Figure 16)
  
  

Figure 17. Navigating to the classification summary

The classifications summary lists every sample, the number of cells in the sample, the number of cells in each classification, and the percentage of cells in each sample that belong to each classification (Figure 17).

 

Figure 18. Viewing the classification summary

With the malignant cells in every sample classified, it is time to save the classifications.

• Click Apply classifications 
• Click Apply when asked to confirm

The pipeline view will open and the Classify cells tasks will run, generating a Classified groups data node and a Group cell counts data node (Figure 18).

 

Figure 19. The Classify cells tasks generates a Classified groups data node

One multi-sample t-SNE plot

For some data sets, cell types can be distinguished when all samples can be visualized together on one t-SNE plot. We will use a t-SNE plot of all samples to classify glioma, microglia, and oligodendrocyte cell types. 

• Click the Filtered counts data node
• Click t-SNE in the Exploratory analysis section of the task menu
• Click the Split cells by sample option under Misc to uncheck it (Figure 19)
  
  

Figure 20. Changing the Split cells by sample option

• Click Apply (Figure 20)
  
  

Figure 21. Setting t-SNE to plot all samples together

•  Click Finish to run the t-SNE task

The t-SNE task will be added as a green layer in the analysis tab (Figure 21). Layers are created in Partek Flow when the same task is run on the same data node. 

 

Figure 22. All samples t-SNE task is added as a new layer

Once the task has completed, we can view the plot.

• Double-click the green t-SNE plot node to open the t-SNE scatter plot

In the multi-sample t-SNE plot, each cell is initially colored by its sample (Figure 22).

 

Figure 23. Viewing the multi-sample t-SNE plot

• Select 2D from the Plot style section

Viewing the 2D t-SNE plot, while most cells cluster by sample, there are a few clusters with cells from multiple samples (Figure 23).

 

Figure 24. Viewing the multi-sample t-SNE plot in 2D

Using maker genes, BCAN (glioma), CD14 (microglia), and MAG (oligodendrocytes), we can assess whether these multi-sample clusters belong to our known cell types. 

• Choose Gene expression from the Color by drop-down menu
• Type BCAN in the new Gene ID box
• Choose BCAN from the list of genes in the data set
• Click the  icon next to BCAN
• Type CD14 in the new Gene ID box
• Choose CD14 from the list of genes in the data set
• Click the  icon next to CD14
• Type MAG in the new Gene ID box
• Choose MAG from the list of genes in the data set

After coloring by these marker genes, three cell populations are clearly visible (Figure 24). 

 

Figure 25. Overlaying marker gene expression on the multi-sample t-SNE plot

• Activate the 3D lasso tool by clicking 
• Draw the lasso around the cluster of red cells and click the circle to close the lasso (Figure 25)
  
  

Figure 26. Classifying microglia (red)

• Click Classify selection

These red cells are CD14 positive, indicating that they are the microglia from every sample. 

• Name the classification Microglia
• Click Save
  

To clearly see the MAG expressing population, clear the current selection.

• Deselect by double clicking on any black space on the plot

Blue MAG expressing cells are the oligodendrocytes from every sample. 

• Draw the lasso around the cluster of blue cells and click the circle to close the lasso (Figure 26)
  
  

Figure 27. Classifying oligodendrocytes (blue)

• Click Classify selection
• Name the classification Oligodendrocytes
• Click Save
• Deselect by double clicking on any black space on the plot

Finally, we will classify the BCAN expressing cells on the plot as glioma cells from every sample.

• Draw the lasso around the green cells and click the circle to close the lasso (Figure 27)
  
  

• Click Classify selection 
• Name the classification Glioma
• Click Save

With every cell from every sample classified, we can view the number of cells classified into each cell type for each sample on the classification summary page. 

• Click Summary 

The fraction of cells of each cell type in each sample is highly heterogeneous (Figure 28). 

 

Figure 29. Viewing classifications for all samples

• Click Apply classifications 
• Click Apply to confirm classification

The pipeline view will open and the Classify cells task will run, generating a new green-layer Classified groups and Group cell counts data nodes (Figure 29). 

 

Figure 30. Classify cells tasks from multi-sample t-SNE plot

 

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