Create a Visualization

Any user can contribute data visualizations to the study and share these with their fellow researchers. You can save these visualizations either via the Advanced Search UI or by using an IDE instance. 

Using advanced search, you can select results of interest, visualize these, and save the visualizations of interest. You can save human metadata visualizations - via either a subjects or samples query - and you can save visualizations of the flow cytometry's supervised gating pipeline - via a result files query.

As an analyst you can also use an R or Python IDE instance to generate visualizations using Ploty, which includes support for the popular ggplot library. There are various sample notebooks available in the IDE instance's 'examples' folder to get started. Alternatively, using a Python IDE you can build complex visualization applications using Dash. For more information on both types of visualizations, see the Building and Saving Visualizations section below.

Regardless of how the visualization was generated, any saved visualization can be retrieved by any user directly in the study UI. In other words, even if the visualization was generated via an IDE instance, no IDE instance is needed to see the saved visualization. Using this workflow, analysts can contribute novel visualizations to a study and share these with (non-coding) scientists for review. 

Each visualization always comes in two forms. First a static image of the visualization is created. This image can be used as a hero image or it can be inserted into a report, e.g., a Google Doc. Second, the original interactive visualization is saved. If the visualization has interactive elements, any study user can load the interactive visualization directly in the UI and do further inspection of the data. 

When visualizations are saved to a study, it is by default saved in "draft" mode. You can use this draft mode to review the results with your collaborators. If you are not quite satisfied with the visualization you can delete it and contribute an improved visualization. Once you are happy with the result, you can switch the visualization to "final". Note that only final visualizations can be used in publications.

If a visualization is generated via an IDE instance, the IDE instance is cloned and saved to the study at the same time. 

Share

The Visualizations tab in a Study is where you can find all Visualizations that were assembled either via Advanced Search or an IDE instance. If you are working on a publication and want to add a Visualization to it, you must promote the Visualization to “final” beforehand. 

There are several types of Visualizations that users can generate and share. Using Advanced Search and HISE’s built-in Visualization feature, users can generate Visualizations based on human metadata, or certain Result Files like flow cytometry or sc-RNA sequencing data. Users can also create an IDE and generate Plotly graphs using R or Python. Lastly, users can generate a complex, interactive Visualization by building a Dash application using Python. 

Built-in Visualizations 

For more information on creating built-in Visualizations via Advanced Search, please visit this document page. 

Users can save any Visualizations they generate via Advanced Search by saving them to a Study. Once in a Study, anyone with access to the Study can view the visualizations by visiting the "Visualizations" tab. 

Plotly Graphs 

Users can create Plotly graphs using either R or Python. Once they have a Visualization they are ready to share and upload to a study, they can use R’s saveVisualization(), or Python’s save_visualization() SDK methods. 

For documentation on the function methods to save Visualizations please refer to the following: 

Once a Plotly Visualization is uploaded to a Study, anyone that has access to the Study can view the Visualization by navigating to the Study within the Collaboration Space. 

Dash Applications

Dash applications have a point-&-click interface to models written in Python, augmenting user capabilities found within a traditional dashboard. General users, as well as scientists and analysts, can easily apply complex Python analytics to datasets.

To create a Dash application, please follow the steps outlined in this document page.

In order to share a Dash application, it must be saved to a Study. We provide a Python SDK for users to upload and share their Visualizations.