Because changes occur when one least expects them. This post is about one such change.

While reading the statistics for my blog, I noticed that a number of searches looked for hierarchical clustering with Python, which [I covered quite a while ago]({{ site.url }}/2007/11/data-clustering-with-python). Today I’d like to present an updated version which uses more robust techniques.

 My Akademy talk proposal was not accepted, but the organizers were kind enough to offer me the chance to hold a BoF on the same subject. Now I bet you wonder on what I’m going to discuss, and I think the title already gives you an idea: KDE and bioinformatics: the missing link Although in the KDE community we have our fair share of scientists (hey there, Stuart!

In the past months I’ve always wanted to write a small Plasma applet to aid me in some boring tasks as a bioinformatician. One example (for the non-scientific crowd out there) is when I find a specific gene out of my analysis work which I want to take a look at. I am often lazy, so instead of firing up the browser to look at the online resources, I wanted to write something which could access said resources programmatically.

Some say that all good things must come to an end. I’m not entirely sure that this is a universal truth, but I can say that at some point in life there are decisions that need to be taken. In this case I made my own: today was the last day inDr.Cristina Battaglia’s laboratory, a place where I spent my three-year Ph.D. course and one year as a post-doc research fellow.

During the course of my research work, I may obtain results that are worthy of publication in scientific journals. Since my master’s thesis I’ve been using LaTeX as my writing platform, mainly because I can concentrate on content rather than presentation (I find it useful also for writing non-scientific stuff as well). Also, I can handle bibliography (essential for a scientific publication) very well without using expensive proprietary applications (such as Endnote).

In my early days I used kLyX first, then LyX, but I found the platform to be too limited for my tastes, and also LaTeX errors were difficult to diagnose. I needed a proper editor, and that’s when I heard of kile, a KDE front-end for LaTeX. Kile is currently at version 2.0.2 and is a KDE 3 application. However, in KDE SVN work is ongoing to produce a KDE4 version (2.1) and that’s what I’ll look at in this entry.

I try to use FOSS extensively for my scientific work. In fact, when possible, I use only FOSS tools. Among these there is the R programming language. It’s a Free implementation of the S-plus language, and it’s mainly aimed at statistics and mathematics. As the people who read my scientific posts know, I don’t like R much. But sometimes it’s the only alternative.

Well, what does R have to do with KDE? With this post I’d like to start a series (hopefully) of articles that deals with KDE programs used for scientific purposes. In this particular entry, I’ll focus on rkward, a GUI front-end for R.

Finally I can lift the curtain of silence and tell the reason why I’ve been very busy before Christmas: it all lies in the publication of a paper, “Using Pathway Signatures as Means of Identifying Similarities among Microarray Experiments”, which is finally out on this week’s issue of PLoS ONE. It’s different from [the previous paper I mentioned]({{ site.url }}/2008/01/phd) (which was not my first publication, either), for two main reasons:

I wanted to write this earier, but I couldn’t: I’m now in a hotel in Maastricht, Netherlands, and waiting to get back tomorrow. I’ve been attending the 4th NuGO hands-on advanced microarray data analysis course and I even wanted to blog about it… but the hotel’s connection did not resolve any non-European web page until late today.

I’ve been wondering about why FOSS is often compared to the academic world, but at least in my limited experience, I see little people that grasp its concept in the world of research. On a quick look, developing FOSS in a research environment would be very good: not only you’d get publicly available results when you publish, but at the same time you can make sure that in an extreme case your application will be carried on by someone else should you not be able to continue development.

I’m often wondering why people only resort to R when working with microarrays. I can understand that Bioconductor offers a plethora of different packages and that R’s statistical functions come in handy for many applications, but still, I think people underestimate the impact of performance. R is not a performing language at all, it doesn’t parallelize well when using HPC (at least from the talks I’ve had with people studying the matter), and in general is a memory and resource hog.

Fourteen days since my last post. Quite a while, indeed. Mostly I’ve been stumbled with work and some health related issues. Anyway, I thought I’d follow up on the meta analysis matter I discussed in my last post. It turns out that it’s a fault of both limma and the data sets, because apparently the raw data found in the Stanford Microarray Database have different length, gene-wise (a result of not all spots on the array being good?

Again in the past days I’ve been banging my head thanks to the fact that doing meta-analysis with microarray data is more difficult than what it seems. The problem sometimes lies in the data, sometimes lies in the analysis software and sometimes in a combination of factors. When doing work on a public data set (Zhao et al., 2005), I had to start analysis from raw data. Now, I tried using both the limma and marray Bioconductor packages, but both of them bail out with cryptic error messages.

The title says it all. After all these years, I was finally able to get my Ph.D. in Molecular Medicine this morning, with my thesis “Identification of disregulated metabolic pathways by transcriptomic analysis in renal carcinoma samples” (yes, that’s a long title). The defense was a success and I admit I was surprised when the commitee actually expressed a significant interest in my work. In any case, I’m happy that it’s over, as the past period has been rather hectic.

There is always a lot of talk about “brain drain” (fuga di cervelli in Italian) from my country. I keep on reading disgruntled comments of low pays and poor research, and that going abroad is the only solution for an Italian scientist to be successful.

While I believe that research done outside of my country can be handled better (but it’s impossible to know for sure: never tar everyone with the same brush), I think that, also thanks to the way the media and the scientists themselves handle it, in everyone’s view it has almost become like the El Dorado. And that, in my opinion, is incorrect.

My supervisor has given me an OK for my thesis (save for a couple of cosmetic changes), therefore now I have just to wait for the verdict of the Ph.D. council then fill in some paperwork: the next step is the defense, sometime in January. After that I’ll probably put my thesis online and post a few articles on the concept of group testing for microarray data.

Today I started working on a data set published on GEO. As the sample data were somehow inconsistent (they mentioned 23 controls when I found 28), I decided to parse the SOFT file from GEO in order to get the exact sample information. I did a grave mistake. First of all, Biopython’s SOFT parser is horribly broken (doesn’t work at all) and quite undocumented: I could work around the lack of documentation (API docs) but not with the fact that it wouldn’t work.

Today I was looking for an easy way to do some calculations of raw expression data on Affymetrix arrays, but I didn’t want to use R: I have already mentioned how I don’t like its design and implementation. While looking for some documentation, I stumbled upon this nifty little program called RMAExpress.