Video Tip of the Week: Pathfinder, for exploring paths through data sets

Pathfinder_scapI didn’t expect to do another tip on the paths through experiments or data this week. But there must be something in the water cooler lately, and all of these different tools converged on my part of the bioinformatics ecosphere. As I was perusing my tweetdeck columns, a new tool from the folks who do the Caleydo projects offered more paths through data: Pathfinder, Visual Analysis of Paths in Graphs.

For years I’ve been celebrating the great visualization options from the Caleydo tools. The first time we highlighted them was 2010. But I’ve been continuing to follow their work and kick the tires when they have new ones. My most recent favorite of theirs was UpSet–a better-than-Venn way to look at sets and subsets among your data.

This new tool offers another way to look across relationships in data sets. Finding paths through data is only getting harder with every new data set we get, but continues to become more important to pull in the characteristics of the alternate routes and yet still have the context of the overall picture. Scaling paths is hard. So the Calydo team aims at several key aspects of the problem with their new Pathfinder tool. The full details are in the paper (cited below), but I’ll list the points for the features they deliver here:

1. Query for paths.
2. Visualize attributes.
3. Visualze group structures in paths.
4. Rank paths.
5. Visual topology context.
6. Compare paths.
7. Group paths.

In addition to clever visualization and query strategies, the team always offers an nice intro video to give you a sense of what the tool can do for you. So the new video on Pathfinder is our Video Tip of the Week.

The example used is the sets of authors on publications. But it’s easy to imagine signalling pathways, or some types of sequence variation pathways, or many other kinds of paths researchers need to represent. They have a use case example in the paper of KEGG pathways. In the video, there’s a quick look at a pathway that includes copy number variations and gene expression data as attributes that may be important for understanding the paths.

Try it out. There’s a demo site available (linked below), and start to think about how you could use Pathfinder to analyze data that you are interested for your research directions.

Hat tip to Alexander Lex for the notice of the new tool:

Quick links:

Pathfinder demo:

Pathfinder overview site:

Source code:


Christian Partl, Samuel Gratzl, Marc Streit, Anne Mai Wassermann, Hanspeter Pfister, Dieter Schmalstieg, & Alexander Lex (2016). Pathfinder: Visual Analysis of Paths in Graphs Computer Graphics Forum ((EuroVis ’16)) In press.