Phaedra 1.0.2

March 15, 2018
By

(This article was first published on Open Analytics, and kindly contributed to R-bloggers)

Phaedra is an open source platform for data capture and analysis of high-content screening data. With the release of Phaedra 1.0.2, we are taking another step towards our goal of unprecedented flexibility in supported setups, ranging from a single small Mac desktop to a cloud-based infrastructure with multiple servers and an array of mixed Windows/Mac/Linux clients.

The initial release of Phaedra supported only the Windows platform. Update 1.0.1 introduced Phaedra on the Mac and Linux desktops, and allowed you to deploy a DataCapture server on Linux servers as well. With update 1.0.2, stability was improved across all platforms, and cloud-based infrastructures are now also supported in the form of pluggable filestore backends. Amazon S3 is supported out-of-the box, as is any S3-compatible store such as Minio. We intend to add more backends, to offer a true choice that can mesh with any corporate infrastructure.

But that’s not all! Take a look at the examples below that showcase other new or improved features in Phaedra 1.0.2.

Creating charts with Python

The Python addon offers a lot of interesting options. It can be used to create advanced calculated features, or for batch operations such as modifying large sets of plates. But with the new Scripted Chart view, you can also use Python scripts to generate charts and display them in Phaedra.

Scripted Python Chart

Many views in Phaedra are reportable, which means you can include them into a PDF report. The Scripted Chart view is no exception: you can save it and insert it into a report with a few clicks.

Scripted Python Chart in PDF report

R-based workflows

Phaedra contains the Knime workflow engine to empower users with an advanced data mining and processing toolbox.
While Knime already has a big scientific community that has created hundreds of free addons, there are still scenarios where you’d want to include your own R-based calculations in a workflow.

For that purpose, there is the R Snippet node: a workflow node that can execute arbitrary R code on a set of input data tables.

Example workflow with R snippet

This R Snippet node uses Phaedra’s embedded R engine, which was just upgraded to version R-3.4.3.

Image rendering with OpenJPEG

At the core of Phaedra’s features are its image rendering capabilities. The ability to view data alongside the image that was the source of the data, is a concept we believe is fundamental to the Phaedra experience.

Various views with image rendering

As these screenshots illustrate, Phaedra places high requirements on the image rendering component. Rendering a huge image in real-time (with the possibility of panning and zooming the image), or rendering dozens of small image crops at once, is no easy task for any image rendering codec.

Phaedra ships with the OpenJPEG codec, which has recently been updated with significant performance improvements.

Documentation has been updated on the project homepage and as always community support on this new release is available on our support site.

To leave a comment for the author, please follow the link and comment on their blog: Open Analytics.

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