Wednesday, May 28, 2014

Pathway analysis for Malaria research

A recurrent theme in my blog is that an easy way to support Open Science is to just join the show. You do not have to contribute a lot to have some impact. Of course, sometimes what you do has more impact than other times. Sometimes something with initially little impact gets high impact later. This is hard to predict, but maybe as well as the stock exchange. In the past I have contributed effort to many Open projects, often small bits, some things never get noticed (like my Ant man page in Debian which is more than 10 years old :).

One project I have long wanted to contribute to, is the Open Source Malaria project, which is brilliantly led by Matt Todd. I had two principle ideas:

  1. use Bioclipse to run the Decision Support against the OSM compounds
  2. do pathway analysis on malaria data
  3. use the AMBIT-JS to put all the OSM compounds online as a HTML page
The first and third I still have not gotten around to finishing. The first is a very simple way for you to contribute. The key question here is just to see how the compounds can be made less toxic / have less side effects. And Bioclipse can visualize this easily, based on various toxicity models, among all those from OpenTox. Really, a four hour job.

PCA results from
for the four sample groups.
The other task is more difficult, and I am really happy that Patricia Zaandam started a ten week internship with me to work on this task. She has been blogging her progress, and I strongly invite you to read her blog and comment (ask questions, post ideas, give criticism), as Open projects are driven by Open communication. Because WikiPathways has most pathways for human, Patricia looking at human expression data. And in five weeks time, she did the preprocessing of the raw data using and did the pathways analysis using PathVisio, resulting in this shortlist of pathways. And now the hard part starts: biological and methodological validation of her approach.

There is plenty of room for feedback. I am not at all a malaria expert, and learning a lot from her study. Some questions we welcome expert input in (as independent test set validation, so to say):
  • what key pathways and genes do we expect to see for treated-versus-ill malaria patients
  • what transcriptomics/proteomics/metabolomics data do you like us to consider too
Etc, etc...

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