Creating a new project and importing the tutorial data set

The tutorial data set is available through Partek® Flow®. 

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

 

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).

 

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. 

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. 

Filtering cells in single cell RNA-Seq data

With samples imported and annotated, we can begin analysis. 

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. 

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. 

 

An important step in analyzing single cell RNA-Seq data is to filer out low quality cells. A few examples of low-quality cells are doublets, cells damaged during cell isolation, or cells with too few reads to be analyzed. 

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. 

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). 

 

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.

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

 

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

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

 

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

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

 

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.

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 this 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.