Dear list,

I have been given with some data to analyze. Unfortunately they only gave 1
replicate per experimental condition, so I do not expect to draw meaningful
information from here. Anyway, I would like to use limma, since I expect
that this could be more powerful than the mere inspection of the log2 fold
change.

Despite I do not have “true biological replicates”,  I think that I can
group (in the design matrix) some arrays as if they were replicates
according to the correlations that I expect from the experimental conditions
and how the data have been generated. For example, I can group 2 arrays that
belong to the same strain, although they have been treated a bit different,
or I can group 2 arrays that belong to the same strain and treatment but
different age of the mouse. This “grouped data” are not going to be part of
the contrast. My intention (and I do not know if it is right) is to group
some correlated data to have some degrees of freedom available to make it
possible the estimates of the variance, and then to make contrasts with
other 2 non replicated arrays.- I think that this would be somehow more
powerful than the log2 fold change inspection, since the information is
better handled trough the empirical Bayes that limma implements, but I would
feel better if someone back me up, because I am not pretty sure if this is a
good idea.


Some piece of my code:

design= model.matrix(~ -1 + factor(c(1,2,3,3,5,6,7,8)))
colnames(design) =c("WT","upa","g1","f5","f6","f7","f8")

            here g1 groups  the same strain (and different from other
strains),  and same age of the mouse but slight different pharmacologicall
treatment, and I will compare f5 vs f6 (this are the same strain and
different from g1, are the same age, but treatment are different)

CM= makeContrasts(f5-f6,levels=design)


Doing this, the M values that I observe in the top list are quite high (>
6), but the differences are not significant. I think that this is due to the
absence of replication in a very noisy sistem.

ID         M         A          t           P.Value            B
23620   mCG147262      -9.0828928978708
 7.04453315872284        -20.6287756557693
        -0.823196144084987
19275   mCG1047122    -6.22956426050092
.91829704792039        -15.5769614644597       1          -0.940793980765775

If I use genefilter to filter out some genes, some genes appear significant
DE though. Would it be possible to explain this just by saying that fdr-like
techniques becomes more sensitive as less comparison are done??

ID         M         A          t           P.Value            B
263       mCG142389      -7.97481171094547
.73475871266083        -5.3168578969303         0.00832939443377308
6.57330274986848
6756     BC027122         -7.40473059624002
.77564203692944        -4.93678117706839       0.0313305586976585
4.89829085664067


I would appreciate any comment or suggestion very much.-
Thank you.

plr.-



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