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For now, the Analyses tab has only a single, circular node, Single cell counts. As you perform the analysis, additional nodes representing tasks and new data will be created, forming a visual representation of your analysis pipeline. A Spatial report task result node (rectangle) is also automatically generated for this type of data. 

• Click the Spatial report node
• Click Task report on the task menu (Figure 7)

The spatial report will display the first sample (Replicate 1). We want to visualize all of the samples. 

• Duplicate the plot by clicking the Duplicate plot button in the upper right controls (Figure 8, arrow 1)
• Open the Axes configuration option (Figure 8, arrow 2)
• Change the Sample on the duplicated image under Misc (Figure 8, arrow 3)

• Click on the Single cell counts node 

A context-sensitive menu will appear on the right-hand side of the pipeline (Figure 9). This menu includes tasks that can be performed on the selected counts data node. 

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An important step in analyzing single cell RNA-Seq data is to filter out low-quality cells. A few examples of low-quality cells are doublets, cells damaged during cell isolation, or cells with too few counts to be analyzed. 

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Click here for more information on Single cell QA/QC

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A task node, Single cell QA/QC, is produced. Initially, the node will be semi-transparent to indicate that it has been queued, but not completed. A progress bar will appear on the Single cell QA/QC task node to indicate that the task is running. 

• Click the Single cell QA/QC node once it finishes running
• Click Task report on the task menu (Figure 7)

The Single cell QA/QC report opens in a new data viewer session. There are interactive violin plots showing the most commonly used quality metrics for each cell from all samples combined (Figure 8).  For this data set, there are two relevant plots: the total count per cell and the number of detected genes per cell. Each Each point on the plots is a cell and the violins illustrate the distribution of values for the y-axis metric. Typically, there is a third plot showing the percentage of mitochondrial counts per cell, but mitochondrial transcripts were not included in the data set by the study authors, so this plot is not informative for this data set.

• Remove the % mitochondrial counts and the extra text box in the bottom right by clicking Remove plot in the top right corner of each plot (Figure 8).

The plots are highly customizable and can be used to explore the quality of cells in different samples. For more information on navigating Single cell QA/QC 

• Click on Single cell counts in the Get Data icon on the left (Figure 9)
• Click and drag the Sample name attribute onto the Counts plot and drop it onto the X-axis 
• Repeat this for the Detected genes plot

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