 From catherine
Microbiologist
National Institute of Agronomic Research
France

Dear all,

Context:
I am a biologist, not a statistician and I need advice for normalization
strategy of a data set.
I want to compare the proteomes of 12 microbial ecosystems under 2
conditions (6 ecosystems per condition)
Each ecosystem is composed of several hundreds of different
microbial species …but surprisingly those ecosystems exhibit
many similar functions that let expect a certain proteome redundancy…
We have 6 ecosystems under condition1 (A1…A6) and 6 ecosystems
under condition2 (B1… B6)
Microbial cell population and its corresponding proteome were
extracted in duplicate from each ecosystem. Previous results
demonstrated a very high similarity between the biodiversity of
duplicate extracts, which let expect good conservation of the
proteomic profile between duplicates
So we have 24 proteomic samples as a whole

Experimental design:
We run 12 2D-DIGE gels with the following experimental design:
         Cy5     Cy3
GelA    A1      B1
GelB    B1      A1
GelC    A2      B2
GelD    B2      A2
GelE    A3      B3
GelF    B3      A3
GelG    A4      B4
GelH    B4      A4
GelI    A5      B5
GelJ    B5      A5
GelK    A6      B6
GelL    B6      A6
A1cy5 and A1cy3 and so on, do not exactly correspond to the so-
called “technical replicates” in microarray experiments, because our
replicates are split from the early extraction step and not just before
labelling
A cy2-labelled composite internal standard (mix of all 24 samples
included in the study) was also included in each gel. We used this
internal standard for image alignment and spot matching only. We
rejected the ratiometric normalization as recommended by several
authors, and we could verify that variance stabilization after
ratiometric normalization was not satisfactory. So we are now in
a two-color multiple gels system.
We have more than 2000 spots matched per image without any
missing or negative intensities after background subtraction, as we
used the SameSpots software for image analyses and intensities
measurements.
The raw intensities (background already subtracted) were exported
for visualization and statistical treatment with R-BioC

Normalization question:
My problem concerns the normalization strategy:
- what am I allowed and not allowed to do?
- In particular, is it opportunistic to first normalize red and green
intensities within each gel (vsn or loess or other???) and then
standardize between gels…, while it is clear from raw intensities
observation (plotMA), that inter-gel replicates are far more similar
than intra-gel pairs. My feeling is that it could be more reliable
to directly normalize between gels using inter-array replicates …
because in this case we are sure that the majority of proteins have
unchanged expression so that assumption for normalization is OK
Would it be statistically correct??
(distorsion between cy5 and cy3 intensities look
like the same whether inter-gel replicates or intra-gel pairs are
considered)
- What would be the best statistical tools for assessment of
normalization : observation of the p-value distribution (or Q-Q plots)
when comparing condition1 and condition2, and perhaps p-value
distribution when permuting observations into group1 (replicates 1)
and group2 (replicates 2) ??? Check the correlation between
replicates??? Compare the variation between groups and the variation
within groups??? Would be the library “lumi” a good tool to do this
(as explained by Lin et al in Nucleic Acids Res 2008) ??

Thank you very much for your help or any suggestion

All the best

catherine


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