The Bioconductor package (http://www.bioconductor.org/) in R (http://www.r-project.org/) is extremely powerful and flexible, not to mention it's free and multiplatform.

Microarray analysis is complex, of course. There are many ways you could do this, but normalization (e.g. rma) then analysis (e.g. affy and limma) are available in R/Bioconductor; don't forget how important it is to assess false discovery rates.

R is worth learning - it's useful generally as a data analysis platform. Tutorials for R are legion, but include:

http://cran.r-project.org/doc/manuals/R-intro.pdf

http://data.princeton.edu/R/gettingStarted.html

(many others)

There are a number of very good tutorials for microarray analysis in R, e.g.:

http://bioinformatics.knowledgeblog.org/2011/06/20/analysing-microarray-data-in-bioconductor/

http://www.bioconductor.org/help/

even videos -

https://www.google.com/search?q=introduction+to+r+microarray&tbm=vid

http://www.youtube.com/user/RafalabChannel?feature=watch

Hope this helps

Hi Marwa

I like Flexarray... it has a pretty and very intuitive interface, runs on windoze and is for free... What do you need more ?.... Good luck. Paco

http://genomequebec.mcgill.ca/FlexArray

The Bioconductor package (http://www.bioconductor.org/) in R (http://www.r-project.org/) is extremely powerful and flexible, not to mention it's free and multiplatform.

Microarray analysis is complex, of course. There are many ways you could do this, but normalization (e.g. rma) then analysis (e.g. affy and limma) are available in R/Bioconductor; don't forget how important it is to assess false discovery rates.

R is worth learning - it's useful generally as a data analysis platform. Tutorials for R are legion, but include:

http://cran.r-project.org/doc/manuals/R-intro.pdf

http://data.princeton.edu/R/gettingStarted.html

(many others)

There are a number of very good tutorials for microarray analysis in R, e.g.:

http://bioinformatics.knowledgeblog.org/2011/06/20/analysing-microarray-data-in-bioconductor/

http://www.bioconductor.org/help/

even videos -

https://www.google.com/search?q=introduction+to+r+microarray&tbm=vid

http://www.youtube.com/user/RafalabChannel?feature=watch

Hope this helps

bioconductor, a package of R language. Or GeneSpring

I do agree with Ray and Wei. You can use R and Bioconductor for this task (http://www.bioconductor.org/packages/2.11/bioc/html/limma.html).

Moreover, you can also try RobiNA (http://mapman.gabipd.org/web/guest/robin).

I hope this helps.

All the best,

If your background is biological (not computer science / engineering / mathematics / statistics), then you might like to learn the Bioconductor package in R and this is the link (http://www.bioconductor.org/)

I use MATLAB for such issues. It's very powerful but you need to get more into computer programming to be able to use it efficiently.

On either way, I don't recommend using some other tools which are so specific. I mean, in R and Matlab, you can use them to achieve a lot of flexible and customised goals; whereas in some other specific tools you would be very limited.

Happy to hear that they are working in microarray data analysis in Egypt.

Best wishes!

If your arrays are Affymetrix arrays you might want to have a look at our arrayanalysis.org for your first steps in quality control and normalisation of your array results (statistics and pathway analysis currently being developed). This webserver implements existing Bioconductor packages but they were modified to better and more consistently work together and the site offers a wealth in documentation about the procedures used and how to interpret the results.

Feedback is very much appreciated.

If you like to program, then R and BioConductor is the choice. If you don't or R is unfamiliar, then MeV is the easiest I have seen, unless you pay the money for GeneSpring, but really if it's the first time you should get advice on the methods to ensure you take the best steps in the right order.

Babelomics http://www.babelomics.org is a WEB platform that includes a complete suite of methods for the analysis of gene expression data: normalization (covering most commercial platforms), pre-processing, differential gene expression (case-controls, multiclass, survival or continuous values), predictors, clustering.

All these genomic data analysis facilities are integrated and connected to multiple options for the functional interpretation of the experiments by using GO, KEGG, Reactome, and also protein–protein networks.

Really I appreciate your kindness and help. I'll try to investigate your suggestions one by one and will see what will happen

Thank you :)

If you are not familiar with programming and arrays I would strongly recommend you to contact any bioinformatician, as Nallasivam Palanisamy says. R and Bioconductor are the best choice but to start you can try MeV or Babelomics which are very user-frendly but not so powerful for statistical analysis.

I started already the analysis but I have a problem which is: there are two types of normalization within array and between arrays \i want to know the difference between both and when I have to use one not the other ???

If you tell me it'll be great or give me a link describe the difference between them

thank you :)

Array experiments involve chemical and photographic processes that always vary slightly.

Some parts of an array will have higher concentrations of chemicals, maybe to top or bottom of the array, or maybe a patch in the middle will be illuminated brighter than the rest of the array, or darker. For this reason you would use within-array normalisation to try to correct for these technical variations caused by the chemical and optical processes - you are aiming to remove technical variation within the array so that you can compare expression values between genes.

Some arrays in an experiment will be illuminated brighter than others, or have slightly different concentrations of chemistry, and this will mean that all the signals from probes are stronger as a whole for one array than another. For this reason you would normalise between arrays, to ensure that each array is comparable with each other array in the experiment.

I think you probably need to use both in all cases.

For beginners mev and brb array tools ( a plugin that allow younwork with R throw excel) will be usefull. Good luck!

As many have suggested a good place to start with is bioconductor packages. Have you tried gsea?

http://www.broadinstitute.org/gsea/index.jsp

Once you extract your expression levels you could use MeV as some people suggested here. There are huge amount of Bioconductor packages and many of these have documentations that help you tremendously. Some times they have data sets incorporated into their packages and in such cases you need to create dat sets in the formats these tools accept, but most of the time most of them use a universal format. If you need to create data set for a particular package and if you don't know how just post it here and we will try to help you. However, I think you would find your way around.