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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 128). For this data set, there are two relevant plots: the total count per cell and the number of detected genes per cell. 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.
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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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For more information on normalizing data in Partek Flow, please see the Normalize countsNormalization section of the user manual.
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