Spatial transcriptomics

Spatial transcriptomics visualizations are normal scatter plots that show how cells are arranged in 2d physical space, rather than how they may cluster by similarity based on an analysis.  SCP allows study owners to pair multiple spatial plots alongside a normal clustering plot to enhance the visual display of information for populations of cells. A live example of the plot shown below can be seen here.

When a study has spatial transcriptomics plots enabled, this will also allow users to inspect gene expression signatures in the spatial data along side the normal clustering.

How do I add spatial data to my study?

If you do not already have a study in SCP, please refer to our guide on Study creation.  This will walk you through how to log into SCP and create a study for the first time.

Once you have a study, you may begin uploading data.  Specifically, spatial transcriptomics files in SCP are cluster files which contain X/Y coordinate data along with names of single cells.  Once you have added cluster files, you will be able to add spatial transcriptomics files.  Look for the spatial section in the upload form and then follow these steps:

1. Choose a spatial transcriptomics cluster file to upload

2. In the "Corresponding cluster" dropdown, indicate the cluster file (if any) that should be displayed side-by-side with your spatial file.  This will control whether this spatial file is displayed by default with the specified cluster.

3. Click "Save" to upload and process your file

Viewing spatial data

Once your spatial transcriptomics file has been processed, you can view it in the "Explore" tab for your study.  To select a spatial plot to be shown side-by-side with any cluster, use the "Spatial groups" menu in the "Options" panel on the right:

Not limited to spatial data

While these plots are labelled as "spatial", it is not necessarily constrained to spatial transcriptomics data.  A study owner can upload any clustering file and use this feature to enable side-by-side viewing of scatter plots.  This can be advantageous for showing complementary analyses, or related populations of cells.