Newbie question about microarray analysis
Austin P. So (Hae-Jin)
nobody at nowhere.com
Sat May 27 12:06:36 EST 2006
Rex Eastbourne wrote:
> Hello,
>
> I apologize in advance for the extreme vagueness of the following
> question -- a friend is asking me if I can help with a project of his
> on protein microarrays, and I don't know anything about the field
> myself.
>
> Here goes: for someone new to the field, how long would it take to
> learn to do some meaningful analysis of protein microarrays with a
> software package? I heard TM4 is good (http://www.tm4.org/). I have a
> strong mathematical background but little knowledge of molecular
> biology. Is this generally the kind of thing that can be picked up in a
> week or so?
Ultimately it depends what you want to do.
There is no need for a molecular biological background if you want to
help your friend to analyze his data. If you know how to analyze a
robust data set in general, then you can help him out.
All you need to be aware of is that essentially the data will be in the
form of a large matrix, typically each row representing a
gene/protein/cell, and each column representing different
sample/time/dose. You want to arrange the data by some algorithm either
by row or by column for all the data points, so that some pattern will
reveal itself. If you have a mathematical model, you can see which data
fit the model. If you don't, you can build a model (most people use a
linear model or a fourier) based on the profile generated. Then let your
friend interpret the results. In some ways it is "better" because you
cannot be biased in your analysis.
One package that is commonly used is "R". The advantage is that it is
just a good statistical package but the learning curve is a bit steep.
Or you can simply use any software that allows you to decompose a data
matrix or cluster it (Eisen's group has freeware that does heirarchical
clustering).
Just be aware that any analysis is only as good as the quality of the
data obtained, and there is no standard in quality for any arrays out
there (i.e. the data is very noisy).
Austin
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