February 28, 2008 – 21:42
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?) and limma itself does need equal length tables to form a single object (I stumbled upon the same problem when doing my thesis, but I used a hack to work around it), and does not perform any checking.
According to the documentation, the “merge” command should be used to deal with these cases, but here’s what I get:
[sourcecode lang='c']
>> RG1 = read.maimages(file=”file1.txt”,source=”smd”)
Read file1.txt
>> RG2 = read.maimages(file=”file2.txt”,source=”smd”)
Read file2.txt
>> merge(RG1,RG2)
Error in merge(RG1,RG2): Need row names to align on
>> rownames(RG1)
NULL
>> rownames(RG2)
NULL [/sourcecode]
I’m going to ask the Bioconductor ML and see what they tell me.
February 14, 2008 – 22:17
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. From what I’ve learnt by googling around, it seems that R doesn’t like batch loading of tables of different length.
I have 177 samples and I have to normalize them all together. Apparently this is a quirk of marray and limma (or worse, R itself) which is preventing me to work properly. And this is not the first time it happens, either: in the past year I’ve lost a lot of time dealing with software issues rather than performing real analsis. The problem has been posted already on some R mailing lists (and on BioC, too), but judging from the responses I doubt I’ll see a solution.
I guess I’ll have to work around this somehow (and of course, this doesn’t improve the idea I have of R…).
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. No time to rest now as I’ve got papers to write and more analyses to do!
And in related news, a recent paper in which I did part of the analysis work is finally out, published in Molecular Cancer. The title is “Genome-wide screening of copy number alterations and LOH events in renal cell carcinomas and integration with gene expression profile” and you can find the provisional PDF on their web page. It’s Open Access, which is definitely a plus.
Today is definitely a nice day from a scientific point of view.
December 22, 2007 – 12:53
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.
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