[BioC] What to do with multiple probes?
krasikov@science.uva.nl
krasikov at science.uva.nl
Fri Nov 25 15:32:41 CET 2005
Dear all,
1.
I have a general question about the multiple probes for each gene.
This question has been discussed several times by BioC community,
but I didn't find any clear solution.
My array platform is bacterial Custom Agilent oligo microarray.
It consists of 8000 unique probes for bit more than 3000 genes (complete
bacterial genome) with 1, 2 or 3 probes per gene (mostly depending on
the length of the gene: 1 for short and 3 for long ones).
The generated list contains statistics for each probe.
What should I do to generate the gene list (which is normally needed for
the biology related research)?
It's fine when the gene is decided to be regulated for all three probes
in the same direction, but what to do if not?
Should I exclude such genes from final list?
May anybody give me a clue how to deal with that?
2. This is for a while my particular solution,
which is maybe far too strict.
My list contain the info like this
(result of the write.fit):
(for three probes for the same gene)
A M p Result Probename
* * * 1 xxx1111_123
* * * 0 xxx1111_566
* * * 1 xxx1111_1050
How to arrange it in elegant way:
A.mean M.mean New.Result xxx1111 M.1 M.2 M.3 p.1 p.2 p.3 ?
where A.mean and M.mean are means of all probes for that gene
and a new Result is logical (something like all three 1 then 1,
all three -1 then -1, if at least one zero or opposite than 0)
3.
For my experiment (in a strictly controlled conditions, with 5
biological replicates and some dye-swaps for them) from my
8000 probes 3500 diceded to be regulated, which is almost half of
complete set (big part of the decisions is biologically relevant,
which is nice).
Is not it to much? (I'm thinking about the statistical assumption that
most of genes should be not changed) However physiologically my
experiment should produce rather big differential expression.
I used direct ratio design, loess and than aquantile normalization,
with BH correction in decideTests and p-value cut-off 0.001.
Thanks in advance for any help.
Vladimir
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