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• Click on Single cell counts in the Get Data card on 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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The cells are now separated into different samples along the x-axis (Figure 10)

• Hold Control and left-click to select both plots
• In Open the Configuration card Style icon on the left , scroll down and expand the Color cardUse the under Configure
• Under Color, use the slider to reduce the Opacity
• In the Configuration card on the left, scroll down and expand the Axis label cardOpen the Axis icon on the left
• Adjust the X-rotation on the plots to 90

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Note how both plots were modified at the same time. 

Cells can be selected by setting thresholds in the Selection card on the rightusing the Select & Filter tool. Here, we will select cells based on the total count

• Open Select & Filter under Tools on the left
• Under Criteria, Click Pin histogram to see the distribution of counts
• Set the Counts thresholds to 8000 and 20500 

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Because this data set was already filtered by the study authors to include only high-quality cells, this count filter is sufficient. 

• Click in the Filtering card on the rightunder Filter to include the selected cells 
• Click Apply observation filter
• Click the Single cell counts data node in the pipeline preview (Figure 12)
• Click Select

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There are three four categories of filter available - noise reduction, statistics based, feature metadata, and feature list (Figure 15).

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The noise reduction filter allows you to exclude genes considered background noise based on a variety of criteria. The statistics based filter is useful for focusing on a certain number or percentile of genes based on a variety of metrics, such as variance. The feature list filter allows you to filter your data set to include or exclude particular genes.

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This produces a Filtered counts data node. This will be the starting point for the next stage of analysis - identifying cell types in the data using the interactive t-SNE plot. 

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