(by hamamun from iut-dhaka.edu)
Mon Jun 7 06:13:00 EST 2010
It seems to me that you have a vary good idea about R and Bioconductor. I'm
a bit new in this R and Bioconductor. could you please tell me how can I get
spot mean intensity and standard deviation from a .CEL file?
Jose de las Heras-2 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 like
> 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 users
> 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 (you
> 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. Apparently
> 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 from gmail.com> wrote in message
> news:1145462502.719792.29860 from 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
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