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. 

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

 

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

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. 

 

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. 

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

 

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. 

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

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. 

 

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

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).

 

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

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). 

 

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

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

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

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

A dialog to give the classification a name will appear. 

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. 

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

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).

 

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

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).

 

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. 

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. 

 

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

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

 

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

 

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

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

 

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

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

Blue MAG expressing cells are the oligodendrocytes from every sample. 

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

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. 

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

 

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).