I have performed a microarray experiment comparing primary monocyte from
mouse fetal livers.

I have three KO samples vs. three WT samples (biological replicates). Each
sample have been splitted into three plates:
- 1/3 (P) have been collected while proliferating (bacteria plate with
GM-CSF)
-1/3 (U) have been let untreated
-1/3 (LPS) have been treated with LPS

Here is a summary of my experiment:

                     Status     treat   liver
WT1.P             WT         U       1
KO1.P             KO          U       A
WT2.P             WT         U       2
KO2.P             KO          U       B
WT3.P             WT         U       3
KO3.P              KO         U       C
WT1.U             WT         U       1
KO1.U             KO          U       A
WT2.U             WT         U       2
KO2.U              KO         U       B
WT3.U             WT         U       3
KO3.U             KO          U       C
WT1.LPS         WT       LPS     1
KO1.LPS         KO        LPS     A
WT2.LPS         WT       LPS     2
KO2.LPS         KO        LPS     B
WT3.LPS        WT        LPS     3
KO3.LPS         KO        LPS     C

The main contrasts that i want to analyze are:
-the difference in basal levels of proliferating cells (KO.P-WT.P)
- the difference between  genes activated by LPS in KO vs WT:
(KO.LPS-KO.U) - (WT.LPS -WT.U)

I did an MDSplot on my samples and they are perfectly separated: the first
dimension separates LPS vs. U or P; while the second dimension separates Wt
vs. KO. Moreover, between WT and KO groups also U and P are separated along
that dimension.

So, now I have some statistical question when fitting a linear model with
limma:
1)Is it correct to fit the linear model considering all the samples? Even
though for example LPS treated sample are completely different from
untreated?
2)Should I consider the experiment with proliferating cells (P) a separate
one and fit an independent linear model (considering that I am not
particularly interested in the differences U-P or LPS - P.
3) Are the statistics influenced by the number of contrasts you investigate
when calling the "contrasts.fit" and "eBayes" functions?
3) When calling lmFit should I "block" for fetal liver origin? (considering
that I split each fetal liver in three plates forming P, U and LPS) even
though cells do not cluster for fetal liver origin).
To that purpose first I calculated the correlation between samples
originating from the same fetal liver (with duplicateCorrelation) and it
was "0.15", afterwards I used lmFit putting the block and correlation
variables into the formula, but when I used "contrasts.fit" I had the
following error:
Error in contrasts.fit(fit2, contrasts2) :
  Number of rows of contrast matrix must match number of coefficients
In addition: Warning message:
In contrasts.fit(fit2, contrasts2) :
  row names of contrasts don't match col names of coefficients

Sorry for all these questions, initially I treated my experiment as a 2x3
factorial, after I conducted two separates analysis one for proliferating
cells and another one as a 2x2 factorial, but now I am not sure on my
statistics.
I hope to have been clear.
Ilario, PhD Student

	[[alternative HTML version deleted]]

