[BioC] How would I normally compare swirl?

[James MacDonald]

In this case you want to do a one-sample t-test where you are testing to
see if the true mean is different from zero. You could simply use the
t.test function in R:

 t.test(c(2.7,2.7,2.7,3))

        One Sample t-test

data:  c(2.7, 2.7, 2.7, 3) 
t = 37, df = 3, p-value = 4.342e-05
alternative hypothesis: true mean is not equal to 0 
95 percent confidence interval:
 2.536317 3.013683 
sample estimates:
mean of x 
    2.775 

You can do that for all the data in an exprSet if you use the esApply
functionality in Biobase. There is a vignette that explains how to do
that for a two-sample t-test, which should give a pretty clear roadmap
for the one-sample case.

HTH,

Jim



James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623

>>> "michael watson (IAH-C)" <michael.watson at bbsrc.ac.uk> 06/02/04
09:26AM >>>
Hi

I have a dataset which is pretty much IDENTICAL to the swirl dataset:

Experiment 1 - two replicate arrays with a dye swap:

TreatedCy5 vs UntreatedCy3
UntreatedCy5 vs TreatedCy3

Experiment 2 - two replicate arrays with a dye swap:

TreatedCy5 vs UntreatedCy3
UntreatedCy5 vs TreatedCy3

This is fantastic because I can basically just copy and paste the
instructions from the limma userguide.pdf document to get my
differentially expressed genes.  

However, I want to do a comparison of limma with a "normal" method of
analysis - say a t-test.  How would I carry out a t-test on this kind
of
data?

For example, the top gene limma pulls out has these values for my four
arrays for log2(Cy5/Cy3):

-2.7, 2.7, -2.7, 3

This makes sense as the experiment contains a dye-swap, so if I flip
my
log(ratios) such that I am always comparing treated/untreated, my
values
are -2.7, -2.7, -2.7 and -3.  BUT how would I go about doing a t-test
on
this kind of comparison???  (I know there are huge arguments against
doing such a thing, but humour me).  I mean, I basically have four
values for the same thing (the relative expression of treated against
untreated) and if I was doing a t-test - what am I comparing the
values
against?  

Thanks in advance

Mick

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