Hi,

Use this doc.

Regards

Hi Hegazy,

You can either use the MATLAB toolbox (http://www.mathworks.de/products/bioinfo/description4.html and http://www.mathworks.de/products/bioinfo/demos.html?file=/products/demos/shipping/bioinfo/cancerdetectdemo.html) or you can also write your own codes in matlab which is very easy not so much complicated.

I can't send files of more than 10MB... do you have an e-mail adress?

Hello El-Sayed,

Well, your questions are really difficult to answer, I guess, since you don't report the kind of files you have to work with. It would be very helpful fo me (us) that you report exactly how your data look like, and what is the file extension of your raw data.

Thank's and good luck!

import RAW files to MATLAB: you have load command for example if you have a data file called data.txt

you can write, 'load data.txt' then you create a variable named data. Matlab is performed for excel files, binaries file. If you want more information about 'load' command write 'doc load' or 'help load'

Next code line is for to load the raw data, considering that X axis is included en the same file. column 1: Axis X, Column: 2,3,... : Axis Y

MM=load('C:\Users\USER\Desktop\prueba.txt','-ascii'); %Load file

figure(1)

h1=plot(MM(:,1),MM(:,2),'s');

xlabel('Text for X axis')

ylabel('Text for the Y axis')

The character between ' ', indicate the point style: s:square. : Triangle:

Example 'rs' red square, 'b>' blue triangle

For identify peaks, there is a peak() function

A lot of thanks for all of you

- Thanks Stiven my mail is [email protected]

- Thanks Mohammad i am trying to follow this tutorials but its not clear enough to finish my task

- Thanks Julia my RAW data extension is .RAW or .MZml

-Thanks Ahmed for comments

-Thanks Juan i will try your code

best regards,

We have importers for .RAW and analyze this kind of data quite often using chemometrics techniques (e.g., via PLS_Toolbox). But your question is difficult because the correct approach is often data/application specific. For example, if you have correlated peaks then you might be able to use multivariate techniques to increase the S/N and do some noise filtering. You might also be interested in Poisson scaling. - good luck!