Hi,

I have an experiment with two experimental groups. In one group there are
three pools of at least 500 worms and in the other there is only one pool
(the sample were pooled out of necessity - i.e lack of rna and lack of
amplification kits).  The three pools from the first group were hybridized
against the single pool in the second group (using a dye swap design and a
total of 6 arrays) and therefore I have 2 df. The design is something like
(A1 -> B,  B -> A1, A2 -> B, B -> A2, A3 -> B, B -> A3). Therefore the
slides can be considered technical replicates (if we consider the second
group pool in each hyb) but are not if we consider the first group pools. I
can see that the hybs are not independent and that this is a problem but
after reading the paper from Kedziorsky on pooling I assume that the
accuracy of this design shouldn't be too bad since the biological
variability between pools should be low (the number of subjects in each pool
is high) in comparison to the technical variability. I analysed the data
using limma with a design matrix:

(1,-1,1,-1,1,-1)

Where 1 and -1 are the dye swaps. Can I trust (at least in part) the p and B
values generated from limma? If not, what kind of criteria can I use to
decide which genes are relevant? Do I have to limit my analysis to fold
change? Can I trust the ranking of genes generated by limma with this
analysis?

I tought about using the variation between the technical replicates as my
best guess for the variation across samples, performing the analysis using
these technical replicates and then using the samples witgh B values >
4.6and fold change > 2 as my criteria for selecting "significant"
genes. Is
this reasonable?

Best regards,

Michael Waisberg

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