Jose de las Heras
josenet at tiscali.co.uk
Thu Apr 27 18:05:26 EST 2006
well, limma allows you to use any generic files as long as they contain a
couple of columns containing channel intensities (and another couple for
background if desired).
if you use limmaGUI, when it comes to selecting what type of file just click
on "other", and it'll open a window asking for the column headers containing
Same thing in limma.
I use something like:
targets<-readTargets(file="Targets file 24-25-28-28-30(SFb0).txt")
RG<-read.maimages(targets$FileName, columns=list(Gf="MedA", Gb="MedBkgA",
and that's it, proceed to your favourite normalisation methods etc.
The only thing you need to make sure is that the order of genes in your
annotation file, where you read the gene IDs from, is the same as in your
quantitation files. In my particular case, I have GAL files with the spot
coordinates, gene names, ID and other interesting stuff I added to it. They
are sorted by Block, then Row and then Column. This is not how SpotFinder
.mev files come out, but if you resort them by MR, then MC, and then by
SR... that's it. You can reort them in limma or from Excel very easily. I
tend to open my .mev files in Excel, sort them, and save them under a
different name... That's what I used to do before I got familiar with R, and
I still do that... You don't need to remove the header, the "#" symbols
protect it and it's not read.
I hope this helps... if not, you can contact me at j.delasCHICKENherasFRIES
@ ed.ac.EGGS.uk (after you remove the edible parts)
"Ress" <stopp_ress at yahoo.com> wrote in message
news:mailman.754.1146168179.16885.methods at net.bio.net...
> Hi Jose,
> Thats a great answer for Guido.
> I also use TM4 but have recently started learning R and limma. I was
> wondering how you can use mev filves in limma because it is not one of the
> file formats e.g. GPR that limma accepts in the command line.
> Jose de las Heras wrote:
>> You could attempt to normalise with Excel. But it's not the best way.
>> If you're going to analyse microarrays, I recommend you use something
>> Limma (linear models for microarray data).
>> You can use Limma to take your raw data and so several type of diagnostic
>> tests to check the quality. Then you can apply a number of types of
>> background correction, normalisation (both within each array for Cy3/Cy5,
>> and between arrays), and producing a list of differentially expressed
>> with stats etc.
>> And all kinds of plots, highlighting genes individually or in groups...
>> In addition, the sorting and subsetting abilities of limma are more
>> and faster than Excel.
>> Ok, the downside is that limma is command-line based. So you do have to
>> spend a little time learning how to use it. But it's easy. There's a
>> guide that takes you step by step through several worked examples showing
>> you how to do most things you'd want to do, and in a couple of days you'd
>> running with your data. It'll be a good investment.
>> Limma is a package from the BioConductor project. BioConductor is a group
>> packages designed for the analysis of microarray data. You'll find apart
>> from limma other tools that will allow you to do clustering analysis,
>> linking to gene ontology databases and all sorts of stuff. I am only
>> familiar with Limma.
>> All these are based around the statistical-oriented programming language
>> All these are free, with extensive help documentation and there's also a
>> BioConductor forum where you can get very useful help if you get stuck
>> All you need to do is go to:
>> and download and install the latest version of R for your platform
>> mac, unix)
>> then, from the same page above, on the left you find a number of links.
>> is "packages". Go there, and download the zip file for "limma". Next, run
>> and from the top menu select "install package from zip file", and select
>> limma one. You're done. Then check the user's guide included in the limma
>> folder and start working through the example.
>> It's also useful to go to the BioConductor site:
>> The BioConductor site has lots of information and there you can find the
>> link to the BioC forum I mentioned. It gets updated less frequently than
>> info in the R project site above, so it's best to get your R and Limma
>> the first website.
>> That would be my preferred option, and one that will serve you well
>> If you find the command-line version of Limma a bit hard going, there's a
>> version with a graphical interface (GUI) called limmaGUI. You can get it
>> If you use windows, you can download a single file that will install R
>> version 2.1.1 and LimmaGUI and all the packages to make it work together
>> one go.
>> This is the simplest way to get started, in 20 minutes, you'll be up and
>> running with your data normalised etc. The problem I see is that the
>> are limited, compared to straight command-line based limma. But you can
>> around it by typing your own commands ina window that you can open from
>> LimmaGUI. Still... if you're going to use limma commands I'd rather do it
>> all from the beginning, but... you may prefer it, check it out.
>> In addition to that, I find the TM4 suite for microarray analysis from
>> very useful.And it's also free. Check it out at:
>> There you get SpotFinder, which you can sue to quantitate your images
>> won't need that as I guess you use GenePix... I also use GenePix now, but
>> started with SpotFinder, and I still go back to SpotFinder a lot. I like
>> you can click on spots on a plot and it'll show you the actual spot
>> intensities, annotation etc... I know GenePix does something similar, but
>> like SpotFinder's evrsion better).
>> You also get MIDAS. MIDAS allows you to normalise data and so some
>> based on a number of conditions. MIDAS takes the output from SpotFinder,
>> you can convert your GPR files to MEV format (the one used by SpotFinder)
>> using their tool ExpressConverter, and then use that for MIDAS.
>> the new version of MIDAS (notout yet) will take GPR files directly, and
>> other nice improvements, but they haven't told me when it'll be out.
>> And you also get TMEV. You can use MEV and GPR files as input. TMEV does
>> clustering analysis and it's quite nice.
>> I mainly use Limma to start, and later use TMEV (either from GPR files of
>> the MEV ones) if I want to do clustering etc.
>> I am very slowly writing a little "easy" guide to use these programs to d
>> some simple data normalisation and analysis, for use in our lab... it
>> me a lot of time if I can give it to a new person and they familiarise
>> themselves before we start. It's still unfinished and has many gaps.. but
>> the Limma and LimmaGUI part is pretty much complete.. if you want it I'll
>> email it to you.
>> I hope this helps!
>> good luck with you arrays
>> "gberna" <gberna at gmail.com> wrote in message
>> news:1145462502.719792.29860 at u72g2000cwu.googlegroups.com...
>>> I have some problems about how to analyze my data:
>>> I'm processing some microarray with protein .
>>> On every slide, I made an hybridization on slide with peptides and my
>>> antibody was colored with Cy3 and Cy5.
>>> In this case the the spots would have to be the same one (becouse cy3
>>> and cy5 are the same one), in fact I see a yellow spot
>>> How can I process this data?
>>> Using excel I calculated log 2 (cy3median-bkg/cy5median-bkg)
>>> How can I normalize the data?
>>> Can you help me?
>>> I want to see the report between for example the prtA_phosfo/prtA (2
>>> peptides on slides)and I don't know which data I must consider.
>>> I hope that someone have understood this delirious post.
>>> sorry for my english
>>> I use GPR file
>> Methods mailing list
>> Methods at net.bio.net
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