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t-SNE (t-distributed stochastic neighbor embedding) is a visualization method commonly used to 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 and or differences in expression of key marker genesgene and pathway expression. 

Multiple single-sample t-SNE plots

Prior to performing t-SNE, it is a good idea to reduce the dimensionality of the data using principal components analysis (PCA).

• Click the Filtered counts data node after the Filter features task
• Select PCA from the Exploratory analysis section of the task menu (Figure 1)

• Click Finish to run PCA with default settings (Figure 2)

Note, the default settings include the Split by sample checkbox being selected. This means that the dimensionality reduction will be performed on each sample separately.

PCA task and data nodes will be generated.

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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 3Figure 1)
  
  

• Click Finish from the t-SNE dialog to run t-SNE with the default settings (Figure 4)

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A t-SNE task

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Because the upstream PCA task was performed separately for each sample, the t-SNE task will also be performed separately for each sample. t-SNE task and data nodes will be generated (Figure 5).

node will be generated (Figure 2).

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

• 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 in a new data viewer session. The t-SNE plot for to the first sample in the data set, MGH36 (Figure 6), will open on the canvasFigure 3). Please note that the appearance of the t-SNE plot may will differ each time it is drawn so your t-SNE plots may will look different than those shown in this tutorial. However; however, the cell-to-cell relationships indicated will be the same. 

The t-SNE plot is in 3D by default. To change the default, click your avatar in the top right > Settings > My Preferences and edit your graphics preferences and change the default scatter plot format from 3D to 2D.  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 on the canvas. We will do this later on in the tutorial.using the Plot style radio buttons. 

Each sample has its own plot. We can switch between samples . 

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using the Back and Next buttons on the

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upper left. 

• Select Next
  

The t-SNE plot has switched to show the next sample, MGH42 (Figure 74). 

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 the expression of known marker genes to identify cell types. 

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

• Select any of the count data nodes from the Data card on the left (Single cell counts, or any of the Filtered counts, Figure 8)
• Search for the BCAN gene
  
• Click and drag the BCAN gene onto the plot and drop it over the Green (feature) optionOpen the Color by drop-down menu
• Select Gene expression from the drop-down menu (Figure 5)
  
  

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)
  
  

The cells will be colored from black to green based on their expression level of BCAN, with cells expressing higher levels more green (Figure 9Figure 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. 

• Select any of the count data nodes from the Data card on the left (Single cell counts, or any of the Filtered counts)
• Search for the GPM6A gene
  
• Click and drag the GPM6A gene onto the plot and drop it over the Red (feature) optionthe Image Added 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 10Figure 8).

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

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

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. 

• Switch to pointer mode modes by clicking  in the top right corner of the plot
• Select a cell by pointing and clicking

The expression level for that cell is displayed on the legend for each gene . Expression values can also be viewed by mousing over a cell (Figure 11(Figure 11). 

• Deselect the cell by clicking on any blank 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. 

Now that cells are colored by the 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 where the majority of cells express BCAN and/with BCAN or GPM6A-expressing cells as the same cell type, even if they do not express either the marker gene.

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

• Activate the 3D lasso tool by clicking  in the top right of the plot
• Draw the lasso around the cluster of green, red, and yellow cells and click the circle to close the lasso (Figure 12)

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• . 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 figure legend. The cells are plotted on the color scale depending on their relative expression levels of the two marker genes (Figure 13 Selection section of the menu.

• Select Classify selection (Figure 12)
  
  

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• Click Classify selection in the Classification card on the right

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• Name the classification Glioma
• Click Save (Figure 14Figure 13)
  
  

Once cells have been classified, the classification is added to the Classifications card on section of the rightpanel. The number of cells belonging to the classification is listed. In ; in MGH42, there are 450 462 glioma cells (Figure 15Figure 14). 

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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 get a better view of allow you to select only cells from the green, red, and yellow cluster
• Switch to lasso mode Activate the 3D lasso tool by selecting  in the top right corner of the plot
• Draw the lasso around the cluster of colored black cells and click the circle to close the lasso (Figure 16Figure 14). 
  
  

• Select Classify selection in the Selection card on the rightselection 
• Type Glioma or select Glioma from the drop-down list (Figure 17prompt (Figure 15)
• Click Save
  
  

• Repeat these steps for each of the 6 remaining samples. Remember to go back to the first sample (MGH36) to classify the glioma cells in that samples too.

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Once all samples have been classified, it is time useful to save the classifications.

• Click Apply classifications in the Classification card on the right
• Click the Filtered counts data node as input data for the classification task (Figure 18)
• Click Select

check the number of cells in each sample assigned to each classification. 

• Click Summary (Figure 16)
  
  

• Name the classification attribute Cell type (sample level) (Figure 19)
• Click Run
• Click OK on the information box that says a classification task has been enqueued

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).

A new task, Classify, is added to the Analyses tab. This task produces a new Classify result data node (Figure 20).

• Click on the Glioma (multi-sample) project name at the top to go back to the Analyses tab
• Your browser may warn you that any unsaved changes to the data viewer session will be lost. Ignore this message and proceed to the Analyses tab

• Double-click the new Classify result data node to open the task report (Figure 21)

The Classification summary table shows a breakdown of the number of glioma cells that were classified per sample. The cells that were not classified are labeled N/A.

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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).

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 on the Glioma (multi-sample) project name at the top to go back to the Analyses tabClick the Filtered counts data node after the Filter features task
• Click PCA t-SNE in the Exploratory analysis section of the task menu
• Uncheck Click the Split cells by sample checkbox option under Misc to uncheck it (Figure 2219)
  
• Click Finish
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The PCA task will run as a new green layer.

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• Click Apply (Figure 20)
  
  

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

The t-SNE task will be added to the as a green layer (Figure 23in 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.

• Double-click the green t-SNE data plot node to open the t-SNE scatter plot
• Click and drag the 2D scatter plot icon onto the canvas and replace the 3D scatter plot (Figure 24)

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In the multi-sample t-SNE plot

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• Search for and select green t-SNE data node (Figure 25)

, each cell is initially colored by its sample (Figure 22).

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• 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 26Figure 23).

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

• Select any of the count data nodes from the Data card on the left (Single cell counts, or any of the Filtered counts)
• Search for the BCAN gene
  
• Click and drag the BCAN gene onto the plot and drop it over the Green (feature) option
• Search for the CD14 gene
• Click and drag the CD14 gene onto the plot and drop it over the Red (feature) option
• Search for the MAG gene
• Click and drag the MAG gene onto the plot and drop it over the Blue (feature) optionChoose 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 Image Added 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 Image Added 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 27Figure 24). 

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

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• Activate the 3D lasso tool by clicking Image Added
• Draw the lasso around the cluster of red cells and click the circle to close the lasso (Figure 28Figure 25)
  
• Click Classify selection
• Name the classification Microglia
• Click Save
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• Click Classify selection

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

• Switch to pointer mode by clicking Image Removed in the top right corner of the plot
• Deselect the cells by clicking on any blank 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 plotSwitch to lasso mode again by clicking the Image Removed icon in the top right of 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 Click (Figure 26)
  
  

• 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.

• Switch to pointer mode by clicking Image Removed in the top right corner of the plot
• Deselect the cells by clicking on any blank space on the plot
• Switch to lasso mode again by clicking the Image Removed icon in the top right of the plot
• Draw the lasso around the cluster of green cells and click the circle to close the lasso
• Click Classify selection
• Name the classification Glioma
• Click Save
• Switch to pointer mode by clicking Image Removed in the top right corner of the plot
• Deselect the cells by clicking on any blank space on the plot

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• (Figure 27)
  
  

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• Click Apply classifications in the Classification card on the right
• Click the Filtered counts data node as input data for the classification task (Figure 30)
• Click Select

• Name the classification attribute Cell type (multi-sample) (Figure 31)
• Click Run
• Click OK on the information box that says a classification task has been enqueued

• 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). 

A new task, Classify, is added to the Analyses tab. This task produces a new Classify result data node in a green layer (Figure 32).

• Click on the Glioma (multi-sample) project name at the top to go back to the Analyses tab
• Your browser may warn you that any unsaved changes to the data viewer session will be lost. Ignore this message and proceed to the Analyses tab

• 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).