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On the System information page, the Download tutorial data section includes pre-loaded data sets used by Partek Flow tutorials (Figure 2). 

 

• Click Glioma (multi-sample) 

The tutorial data set will be downloaded onto your Partek Flow server and a new project, Glioma (multi-sample), will be created. You will be directed to the Data tab of the new project. Because this is a tutorial project, there is no need to click on Import data, as the import is handled automatically (Figure 3). 

 

You can wait a few minutes for the download to complete, or check the download progress by selecting Queue then View queued tasks... to view the Queue (Figure 4).

 

Once the download completes, the sample table will appear in the Data tab (Figure 5).

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SubtitleText Invoking the Single Cell QA/QC task AnchorName Invoking Single Cell QA/QC

One metric for analyze cell quality is the percentage of mitochondrial reads. If a cell has a high percentage of mitochondrial reads, it is likely undergoing apoptosis and should be excluded from analysis. To calculate the mitochondrial reads percentage, the counts matrix needs to be associated with a relevant genome assembly and a gene/feature annotation with mitochondrial transcripts (Ensembl or GENCODE).  

• Select Homo sapeins (human) - hg19 from the Assembly drop-down menu
• Select Ensembl Transcripts release 75 from the drop-down menu
• Select Click Finish (Figure 15)
  
  

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SubtitleText Specifying the assembly and annotation for Single-cell QA/QC AnchorName Specifying assembly and annotation

A task node, Single cell QA/QC, is produced.  

• Click the Single cell QA/QC node
• Click Task report on the task menu (Figure 16)
  
  

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SubtitleText Selecting the task report for any task node opens a report with any tables or charts the task produced AnchorName Invoking Single Cell QA/QC task report
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The Single cell QA/QC report includes interactive violin plots showing the value of every cell in the project on several quality measures (Figure 17).

 

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SubtitleText Each cell is shown as a point on the plot. AnchorName Single cell QA/QC report
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 For this data set, there are two plots: number of reads per cell and number of detected genes per cell. Typically, there is a third plot showing the percentage of mitochondrial reads per cell, but mitochondrial transcripts were not included in the data set by the study authors.

Each point on the plots is a cells and the violins illustrate the distribution of cell values. Cells can be filtered either by drawing a gate on one of the plots or by setting thresholds using the filters below the plots. Here, we will apply a filter for the number of read counts.

• Set the Read counts filter to Keep cells between 8000 and 20500 reads 

The plot will be shaded to reflect the gate. Cells that are excluded will be shown as black dots on both plots (Figure 18). 

 

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SubtitleText Previewing a filter using the Single cell QA/QC violin plots AnchorName Filtering cells by read counts
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 Because this data set was already filtered by the study authors to include only high-quality cells, this read counts filter is sufficient for this tutorial. 

• Select Apply filter 

A new task, Filter cells, is added to the Analyses tab. This task produces a new Single cell data node (Figure 19).

 

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SubtitleText Applying a cell quality filter AnchorName Output of Filter cells
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For more information about Single Cell QA/QC, please see our user manual section. 

Filtering genes in single cell RNA-Seq data

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