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• Click Settings 

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

• Click Single cell 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, with one row per sample (Figure 5).

Annotating samples with attributes

The Data tab displays the samples in the project with the number of cells in each sample (Figure 5). One of the goals of this analysis will be to compare gene expression in a cell type between the two Glioma subtypes. For this, we need to add an annotation indicating the subtype of each sample. 

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• Type Astrocytoma in the New category text field
• Click Add
• Type Oligodendroglioma in the New category text field
• Click Close
• Click Back to sample management table

There is new column, Subtype, in the Data tab, but every sample has a value of N/A. Next, we will assign each sample to a subtype. 

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The sample table is pre-populated with two sample attributes: # Cells and Subtype. Sample attributes can be added and edited manually by clicking Manage in the Sample attributes menu on the left. If a new attribute is added, click Assign values to assign samples to different groups. Alternatively, you can use the Assign values from a file option to assign sample attributes using a tab-delimited text file. For more information about sample attributes, see here.

For this tutorial, we do not need to edit or change any sample attributes.

Filtering cells in single cell RNA-Seq data

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For now, the Analyses tab has only a single node, Single cell datacounts. As you perform the analysis, additional nodes representing tasks and new data will be created, forming a visual representation of your analysis pipeline. 

• Click on the Single cell data 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. 

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 reads counts to be analyzed. 

• Click on Expand the QA/QC section of the task menu
• Click on Single cell QA/QC (Figure 106)
  
  

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 11Figure 7)
  
  

The Single cell QA/QC report includes opens in a new data viewer session. There are interactive violin plots showing the value of every cell in the project on several quality measures (Figure 12). 

For most commonly used quality metrics for each cell from all samples combined (Figure 8). For this data set, there are two relevant plots: number of reads 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 reads 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).

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

The plot will be shaded to reflect the filter. Cells that are excluded will be shown as black dots on both plots (Figure 13). 

The plots are highly customizable and can be used to explore the quality of cells in different samples.

• Click on Single cell counts in the Data card 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

The cells are now separated into different samples along the x-axis (Figure 10)

• Hold Control and left-click to select both plots
• In the Configuration card on the left, scroll down and expand the Color card
• Use the slider to reduce the Opacity
• In the Configuration card on the left, scroll down and expand the Axis label card
• Adjust the X-rotation on the plots to 90

Cells can be selected by setting thresholds in the Selection card on the right. Here, we will select cells based on the total count

• Set the Counts thresholds to 8000 and 20500 

Selected cells will be in blue and deselected cells will be dimmed (Figure 11). 

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

• Click Apply filter 

• Click Image Addedin the Filtering card on the right
• Click Apply filter
• Click the Single cell counts data node in the pipeline preview (Figure 12)
• Click Select

A new task, Filter cellscounts, is added to the Analyses tab. This task produces a new Single cell data node (Figure 14).

Filter counts data node (Figure 13).

• 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

Most tasks can be queued up on data nodes that have not yet been generated, so you can wait for filtering step to complete, or proceed to the next section. 

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A common task in bulk and single-cell RNA-Seq analysis is to filter the data to include only informative genes. Because there is no gold standard for what makes a gene informative or not and , ideal gene filtering criteria depend depends on your experimental design and research question. Thus, Partek Flow has a wide variety of flexible filtering options. 

• Click the Single cell data Filter counts node produced by the Filter cells counts task
• Click Filtering in the task menu
• Click Filter features (Figure 15Figure 14)
  
  

There are three categories of filter available - noise reduction, statistics based, and feature list (Figure 16Figure 15). 

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.

We will use a noise reduction filter to exclude genes that are not expressed by any cell in the data set , but were included in the matrix file.

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• Click the Noise reduction filter check box checkbox 
• Set the Noise reduction filter to Exclude features where value <= 0 in 99% of cells using the drop-down menus and text boxes (Figure 16)
• Click Finish to apply the filter
  
  

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The tutorial data set is taken from a published study and has already been normalized using TPM (Transcripts per million), which normalizes for the length of feature and total reads, and transformed as log2(TPM/10+1). This normalization and transformation scheme can be performed in Partek Flow, along with other commonly used RNA-Seq data normalization methods. 

For more information on normalizing data in Partek Flow, please see the Normalize counts section of the user manual.  

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