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Using the t-SNE plot, cells can be classified based on clustering results and differences in expression of key marker genes. 

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).

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

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PCA task and data nodes will be generated.

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

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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).

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

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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. 

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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 the malignant cells we want to include and which cells are the normal cells we want to exclude. 

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

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

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

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

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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/or GPM6A as the same cell type, even if they do not express either marker gene.

• Switch to lasso mode by clicking Image RemovedImage Added 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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Selected cells are shown in bold and unselected cells are dimmed. 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)

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• Click Classify selection in the Classify icon under Tools

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Once cells have been classified, the classification is added to Classify. The number of cells belonging to the classification is listed. In MGH42, there are 460 glioma cells (Figure 15). 

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Classifications made on the t-SNE plot are retained as a draft as part of the data viewer session. In this tutorial, we will classify malignant cells for each sample before we save and apply the classifications, but if necessary, you can save the data viewer session by clicking the Image RemovedImage Added Save icon on the left to retain all of the formatting and draft classifications. The data viewer session will be stored under the Data viewer tab and can be re-opened to continue making classifications at a later time. 

• Switch to pointer mode by clicking Image RemovedImage Added in the top right corner of the plot
• Deselect the cells by clicking on any blank space on the plot
• Open Axes and navigate to Sample under Misc
• Select the Image RemovedImage Addedicon below the sample name to go to the next sample, MGH45
• Rotate the 3D t-SNE plot to get a better view of cells from the green, red, and yellow cluster
• Switch to lasso mode by selecting Image RemovedImage Added in the top right corner of the plot
• Draw the lasso around the cluster of colored cells and click the circle to close the lasso (Figure 16)

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• Select Classify selection in the Classify icon
• Type Glioma or select Glioma from the drop-down list (Figure 17)
• Click Save

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

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Numbered figure captions
SubtitleText Name the cell-level attribute AnchorName Classifiy attribute name
Image RemovedImage Added

The new attribute is stored in the Data tab and is available to any node in the project.

• 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

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. 

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The PCA task will run as a new green layer.

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

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

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• In the Style icon, choose Sample name from the Color by drop-down list under Color

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

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

• Switch to lasso mode by clicking the Image RemovedImage Added icon in the top right of the plot
• Draw the lasso around the cluster of red cells and click the circle to close the lasso (Figure 28)
• Open the Classify tool and click Classify selection
• Name the classification Microglia
• Click Save

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The blue cells are MAG positive, indicating that they are the oligodendrocytes from every sample. 

• Switch to pointer mode by clicking Image RemovedImage Added 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 RemovedImage Added icon in the top right of the plot
• Draw the lasso around the cluster of blue cells and click the circle to close the lasso
• Open the Classify tool and click Classify selection
• Name the classification Oligodendrocytes
• Click Save

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• Switch to pointer mode by clicking Image RemovedImage Added 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 RemovedImage Added 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
• Open the Classify tool and click Classify selection
• Name the classification Glioma
• Click Save
• Switch to pointer mode by clicking Image RemovedImage Added in the top right corner of the plot
• Deselect the cells by clicking on any blank space on the plot

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• Click Apply classifications in the Classify icon (Figure 30)

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• Name the classification attribute Cell type (multi-sample) (Figure 31)
• Click Run

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The new attribute is now available for downstream analysis.

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