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

• Scroll down on the Configuration card on the left and expand the Data card Open the Axes icon on the left under Configure (Figure 7)
• Navigate to Misc 
• Select the icon below the sample Sample name to go to the next sample
  

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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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• Select any of the count data nodes from the Data card on Get data 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) option

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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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• Click Classify selection in the Classification card on the right in the Classify icon under Tools

A dialog to give the classification a name will appear.

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Once cells have been classified, the classification is added to the Classifications card on the right Classify. The number of cells belonging to the classification is listed. In MGH42, there are 450 460 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 Removed save Image 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  in the top right corner of the plot
• Deselect the cells by clicking on any blank space on the plotScroll down on the Configuration card on the left and expand the Data card
• Open Axes and navigate to Sample under Misc
• Select the icon 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  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 Selection card on the rightthe 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.

There should be 5,348 322 glioma cells in total across all 8 samples.  

• The classification name can be edited or deleted (Figure 18). 

With the malignant cells in every sample classified, it is time 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

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

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

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

• 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.

The Top marker features per label table shows the top 10 upregulated genes in each cell type. In this case, the glioma cells are compared to the N/A cells using ANOVA and the genes are filtered for fold-change >1.5 and sorted by descending fold-change values. To obtain the full list of biomarker genes with p-values and fold-changes, click the Download link in the bottom right of the table.

One multi-sample t-SNE plot

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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 Configuration card, expand the Color card and Style icon, choose Sample name from the Color by drop-down list under Color

Viewing the 2D t-SNE plot, while most cells cluster by sample, there are a few clusters with cells from multiple samples (Figure 26).

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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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• Switch to lasso mode by clicking the  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)
• Click Classify Open the Classify tool and click Classify selection
• Name the classification Microglia
• Click Save

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• Switch to pointer mode by clicking  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  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
• Click Classify Open the Classify tool and click Classify selection
• Name the classification Oligodendrocytes
• Click Save

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

The number of cells classified as microglia, oligodendrocytes, and glioma are shown in the Classification card on the right Classify (Figure 29)

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

Image Removed

• 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

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).The new attribute is now available for downstream analysis.

• 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

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