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Create a new project and

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transfer files to the server

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

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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

With samples imported and annotated, we can begin analysis. 

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

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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. 

Normalizing single cell RNA-Seq data

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

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