Hello everybody
After successful importing the data to the LIMMA,
I followed all steps and finally obtained "topTable".
(the data were 44K Agilent)
I'm surprised that my data set doesn't have anything with M-values
more
than 1.5.
I well understand that it depends on the experiment,
but is there some condensation of the Ratio during normalization and
other procedures?
regards
--
Dr. Nataliya Yeremenko
Universiteit van Amsterdam
Faculty of Science
IBED/AMB (Aquatische Microbiologie)
Nieuwe Achtergracht 127
NL-1018WS Amsterdam
the Netherlands
tel. + 31 20 5257089
fax + 31 20 5257064
Hi, Nataliya.
This is very much dependent on how you normalize your data. It's
possible to "over normalize" and wipe out most of the variability in
your data. If you're certain that this isn't the case, then, no,
Limma doesn't generate shrunken estimates of the expression ratios,
only t-statistics. The expression ratios presented by limma will
either be log2, or a data dependent scale if you've used VSN to
normalize. Feel free to post more information about your
normalization / preprocessing / experimental design, that would help
to identify potential reasons, otherwise we're just speculating.
Cheers,
--------------------------------------------
Greg Finak
PhD Candidate
McGill Center For Bioinformatics
W: (514)398-7071 x09317
emai: finak at mcb.mcgill.ca
--------------------------------------------
On 9-Nov-05, at 7:13 PM, Nataliya Yeremenko wrote:
> Hello everybody
>
> After successful importing the data to the LIMMA,
> I followed all steps and finally obtained "topTable".
> (the data were 44K Agilent)
> I'm surprised that my data set doesn't have anything with M-values
> more
> than 1.5.
> I well understand that it depends on the experiment,
> but is there some condensation of the Ratio during normalization and
> other procedures?
>
> regards
>
> --
> Dr. Nataliya Yeremenko
>
> Universiteit van Amsterdam
> Faculty of Science
> IBED/AMB (Aquatische Microbiologie)
> Nieuwe Achtergracht 127
> NL-1018WS Amsterdam
> the Netherlands
>
> tel. + 31 20 5257089
> fax + 31 20 5257064
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/bioconductor
Dear Greg
Thanks for your comment:
Detailed description and a number of problems are described in
my new BioC post with subject: Design question in LIMMA
As for data preporcessing step I used ordinary:
RG <- backgroundCorrect(RG,method="minimum")
MA <-normalizeWithinArrays(RG, method="loess")
MA <- normalizeBetweenArrays(MA, method="Aquantile")
I didn't use weighting of the control spots (commercial Agilent 44K)
and didn't used anything flaged by Feature Extraction.
Maybe I have to?
I visualize the data afterwards and they look not bad.
I do understand that the data in topTable are in log2 scale,
but the differences seem to me to be really small.
The problem may come as well from the wrong design and contrasts
created.
I hope this makes clear procedures I used.
Regards
Nataliya
Greg Finak wrote:
> Hi, Nataliya.
>
> This is very much dependent on how you normalize your data. It's
> possible to "over normalize" and wipe out most of the variability in
> your data. If you're certain that this isn't the case, then, no,
> Limma doesn't generate shrunken estimates of the expression ratios,
> only t-statistics. The expression ratios presented by limma will
> either be log2, or a data dependent scale if you've used VSN to
> normalize. Feel free to post more information about your
> normalization / preprocessing / experimental design, that would help
> to identify potential reasons, otherwise we're just speculating.
>
> Cheers,
>
>
> --------------------------------------------
> Greg Finak
> PhD Candidate
> McGill Center For Bioinformatics
>
> W: (514)398-7071 x09317
> emai: finak at mcb.mcgill.ca
> --------------------------------------------
>
> On 9-Nov-05, at 7:13 PM, Nataliya Yeremenko wrote:
>
>> Hello everybody
>>
>> After successful importing the data to the LIMMA,
>> I followed all steps and finally obtained "topTable".
>> (the data were 44K Agilent)
>> I'm surprised that my data set doesn't have anything with M-values
more
>> than 1.5.
>> I well understand that it depends on the experiment,
>> but is there some condensation of the Ratio during normalization
and
>> other procedures?
>>
>> regards
>>
>> --
>> Dr. Nataliya Yeremenko
>>
>> Universiteit van Amsterdam
>> Faculty of Science
>> IBED/AMB (Aquatische Microbiologie)
>> Nieuwe Achtergracht 127
>> NL-1018WS Amsterdam
>> the Netherlands
>>
>> tel. + 31 20 5257089
>> fax + 31 20 5257064
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>
>
>
>
--
Dr. Nataliya Yeremenko
Universiteit van Amsterdam
Faculty of Science
IBED/AMB (Aquatische Microbiologie)
Nieuwe Achtergracht 127
NL-1018WS Amsterdam
the Netherlands
tel. + 31 20 5257089
fax + 31 20 5257064
On 11/9/05 7:13 PM, "Nataliya Yeremenko" <eremenko at="" science.uva.nl="">
wrote:
> Hello everybody
>
> After successful importing the data to the LIMMA,
> I followed all steps and finally obtained "topTable".
> (the data were 44K Agilent)
> I'm surprised that my data set doesn't have anything with M-values
more
> than 1.5.
> I well understand that it depends on the experiment,
> but is there some condensation of the Ratio during normalization and
> other procedures?
>
> regards
Nataliya,
If I understand your original question, you are asking about M-values
as
reported by topTable? These M-values are the log2 fold-change
associated
with the contrast associated with that coefficient. In other words,
if you
are doing a two-group comparison in limma (as a simple example), the
M-value
represents the average log2 fold-change between the groups. If you
are not
getting large M-values, then you do not have large differences between
your
groups.
Of course, normalization can have a significant effect on the fold-
changes
that you see in topTable, but another likely explanation,
normalization
aside, is that there are not large differences between groups.
I may have misunderstood your question, but I'm not sure that
normalization
is the culprit here. And I think that most would advocate doing
normalization based on biologic understanding of the experiment and
global
characteristics of the data and not the resulting fold-changes or
significance; it really probably isn't a good idea to do multiple
normalizations looking for the one that gives the best fold-change or
gene
list (not that you were going to do so).
Hope this helps,
Sean