We have a lot of old microarray data but no longer have a license for Genespring. I can use Excel to look at the individual Imagene files (Cy3 or Cy5 for each array hybridization), but this is very cumbersome, because I have to look up the intensity for each gene in each file separately.
Does anyone know of free software that will take the Cy3 and Cy5 Imagene files and show you the Cy3/Cy5 ratio for each gene? Preferably something that will run on a Mac?
We could try to again take advantage of Genespring's free 30-day trial, but I'd actually prefer something that was less trouble to set up. I don't need to have a genome view, or to have the software know the genome sequence that underlies the genes. And I don't want to do any fancy clustering analysis - I just want to see the intensity ratios.
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I'm assuming that these are differential gene expression analyses? If so, the first option that springs to mind is Bioconductor, particularly the limma package. It's got a great user manual with step-by-step examples illustrating what to do with common experimental designs, including how to choose a normalization method and how to deal with your replicates.
ReplyDeleteKeith, as soon as I looked at Bioconductor I realized that you're overestimating both my skillset and the size of the task. I don't know R at all, and don't plan to learn it (definitely not for this small project). I just want to take a quick look at the relative expression of a few genes under a few conditions.
ReplyDeleteBut I realize that, from the perspective of microarray experiments, what I want to do is so shabby that probably nobody has ever stooped to writing a program to do it.
My fallback is Excel. I can paste in the Cy3 and Cy5 data for the purine genes from the Imagene files, and then just look at the ratios. This will probably take less time than I'll spend searching for the files with the data I want, and far less than learning to do the analysis properly in Bioconductor.
(What can I say, I'm a weak and lazy person...)
I will get the post-doc (now R-trained) to take a look at Bioconductor, because it certainly would be nice to have easy access to all our old microarray data.
ReplyDeleteThat's good news.
ReplyDeleteI should have added a disclaimer that I work in a microarray informatics group, so I have a biased view! OTOH, I'm an ex-bench-microbiologist, so I understand that you have other demands on your time.
You can look at raw intensities if you like, but you are almost certainly not comparing "relative expression", but something closer to "relative RNA abundance, RNA extraction and labelling efficiencies, dye effects, other systematic effects and noise".
Caveat emptor, etc.