Creating a new project and importing the tutorial data set

The tutorial data set is available through Partek Flow. 

• Click your avatar (Figure 1)
  
  

Figure 1. Location of the Settings link on the main page of Partek Flow

• Click Settings 

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

 

Figure 2. Tutorial data sets available through Partek Flow

• 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). 

 

Figure 3. The data tab during tutorial data import

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).

 

Figure 4. Viewing the queue

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

 

Figure 5. Sample data table listing the name and the number of cells for each sample

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. 

• Click Manage attributes 
• Click Add new attribute (Figure 6)
  
  

Figure 6. Adding an attribute

• Type Subtype in the Name text field
• Click Add (Figure 7)
  
  

Figure 7. Adding Subtype as an attribute

• 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 samples a value of N/A. Next, we will assign each sample to a subtype. 

• Click Edit attributes 

• Use the drop-down menus to assign each sample to its corresponding subgroup (Figure 8)
  
  

  Sample Name	Subtype
  MGH36	Oligodendroglioma
  MGH42	Astrocytoma
  MGH45	Astrocytoma
  MGH53	Oligodendroglioma
  MGH54	Oligodendroglioma
  MGH56	Astrocytoma
  MGH60	Oligodendroglioma
  MGH64	Astrocytoma

  
  
  

Figure 8. Assigning samples to subtypes

• Once each sample has been assigned to a subgroup, click Apply changes to proceed

Filtering cells in single cell RNA-Seq data

With samples imported and annotated, we can begin analysis. 

• Click Analyses to switch to the Analyses tab

For now, the Analyses tab has only a single node, Single cell data. 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 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 data node. 

 

Figure 9. Clicking on a data node opens the context-sensitive task menu

An important step in analyzing single cell RNA-Seq data is to filer out low quality cells. Low quality cells can be doublets, cells damaged during cell isolation, and cells with too few reads to be analyzed. 

• Click on QA/QC section of the task menu
• Click on Single cell QA/QC (Figure 10)
  
  

Figure 10. Selecting the Single cell QA/QC task from the task menu

A task node, Single cell QA/QC, is produced. Initially, the node will be semi-transparaent 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 11)
  
  

Figure 11. Selecting the task report for any task node opens a report with any tables or charts the task produced

The Single cell QA/QC report includes interactive violin plots showing the value of every cell in the project on several quality measures (Figure 12). 

 

Figure 12. Each cell is shown as a point on the plot.

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 cell and the violins illustrate the distribution of values for the y-axis metric. Cells can be filtered either by clicking and dragging to select a region 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 13). 

Figure 13. Previewing a filter using the Single cell QA/QC violin plots

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. 

• Click Apply filter 

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

 

Figure 14. Applying a cell quality filter

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. 

Filtering genes in single cell RNA-Seq data

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 criterea depend on your experimental design and research question, Partek Flow has a wide variety of flexible filtering options. 

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

Figure 15. Invoking Filter features

There are three categories of filter available - Noise reduction filters, Statitics bsaed filters, and Feature list filters (Figure 16).

 

The Noise reduction filter allows you to exclude genes considered background noise based on a variety of criteria. The Statistics based filters are 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 expresed by any cell in the data set, but were included in the matrix file.

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

Figure 17. Configuring a noise reduction filter to exclude genes not expressed in the data set

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. 

Normalizing single cell RNA-Seq data

We are omitting normalization in ths tutorial because the data has already been normalized. 

The tutorial data set is taken from a published study and has already been normalized using TPM (Transcripts per million), which normalizes for 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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