Question: RMA normalization,which samples should be normalized together

0

Dipl.-Ing. Johannes Rainer •

**430**wrote:thank you naomi,
this is exactly what i've done til now, first i have normalized each
patient seperatly (or some patients together if i had three chips of
one patient (as RMA works not so good with a small uneven number of
chips)), and then when we finished to do the chips from all patients i
normalized them all togehter.
in the first analysis i searched for the genes that were regulated
(bigger or smaller then M=1 (M=-1 respectively)) in most patients. the
genes found by this method are fairly the same like those i got when
normalizing all patients together and doing a wilcoxon test.
that's all fine, but my boss was surprised to see that our replicates
(=quality controls) looked better in the "single patient normalization
way" than after normalizing all patients toghether.
well, thanks for your answer, i think i will normalize all chips
together and my boss has to accept that our controls are not as nice
as
they should be... thanks, jo
Quoting Naomi Altman <naomi@stat.psu.edu>:
> Dear Johannes,
> Actually, technical replication is of little interest when you have
> biological replication. If I understand your experiment, you have
40
> patients, each measured at 2 times.
>
> Because of the pairing, you have several options for appropriate
> normalization and analysis:
>
> 1) Normalize the before and after for each patient together, and
analyze M.
>
> You could use either RMA, or a simpler M vs A loess for this.
>
> 2) Normalize all the arrays together and then compute M for each
patient.
>
> I would use RMA or gcRMA for this.
>
> In either case, I would simply use limma with the contrast
> rep(1,npatients) since this gives the t-test for before-after which
> seems to be of most interest. Limma has an advantage over ordinary
> t-tests in that it combines some information across genes. However,
> I expect it to be very similar to ordinary t-tests (or Wilcoxon
> tests) because you have a fairly large sample size. Any of these
> methods are appropriate.
>
> Incidentally, the technical reps are interesting for quality
control,
> but should not be included in this analysis.
>
> --Naomi
>
> At 09:48 AM 2/7/2005, Dipl.-Ing. Johannes Rainer wrote:
>> thanks arne
>>
>> i have no replicates, affymetrix is still a little bit expensive ;)
>> . all our chips were made by ourself and by looking at the
>> histograms of the raw values there are no differences at all. in
the
>> whole experiment we made also two replicates, one with the same
RNA,
>> but different amount before amplification (one time 5 mug, the
>> second time 1 mug) and the second replicate is RNA from the same
>> patient, same time point, but the RNA was extracted by two
different
>> people not at the same time. if i normalize only those replicated
>> chips i see nearly no differences between them (with a M (log2
>> regulation value) cut off of M=1 i get about 30 probe sets that
>> differ), but when i normalize all 80 chips of all patients together
>> the replicated chips show more differences... in my opinion i have
>> to normalize all patient chips together, exspecially if i want to
do
>> for example a wilcox between all 0 hour and 6 hours chips.
>> can you tell me a little bit more about the linear model you have
>> used to merge the results?
>>
>> regards, jo
>>
>>
>> Quoting Arne.Muller@sanofi-aventis.com:
>>
>>> Dear Johannes,
>>>
>>> I've a study with 84 affy chip to characterize a dose effect of a
>>> drug. The study was conducted in 3 different laboratories. There
>>> are strong differences betweent the laboratories and I've RMA
>>> normalized per laboratory and then merged the results in a single
>>> linear moel including the laboratory as an additional factor.
Maybe
>>> you can make the patient or source of RNA a random factor in a
mixe
>>> effects model - if you've replication per patient.
>>>
>>> Just looking at those genes with a significant dose effect I did
>>> not find much differences between normalizing all chips together
and
>>> normalizing per laboratory.
>>>
>>> regards,
>>>
>>> Arne
>>>
>>>
>>>> -----Original Message-----
>>>> From: bioconductor-bounces@stat.math.ethz.ch
>>>> [mailto:bioconductor-bounces@stat.math.ethz.ch]On Behalf Of
Dipl.-Ing.
>>>> Johannes Rainer
>>>> Sent: 07 February 2005 10:13
>>>> To: bioconductor@stat.math.ethz.ch
>>>> Subject: [BioC] RMA normalization,which samples should be
normalized
>>>> together
>>>>
>>>>
>>>> hi,
>>>> we are interested in the response of patients to a special
treatment,
>>>> so we have patient samples before and after treatment. i have
>>>> normalized this samples in different ways using RMA. As RMA tries
to
>>>> detect and correct probe effects by looking at the expresison
>>>> levels of
>>>> the probes across all chips it is not surprising that the outcome
of
>>>> the analysis differs depending on which chips i normalize
together.
>>>> It is clear that i have to normalize all patient samples
>>>> together if i
>>>> want to compare the expression values of the genes (lets say
using
>>>> statistical tests). i am also analyzing the chips using the 'old
>>>> fashioned way' by using M and A values and i suppose it is not
>>>> problematic at all to compare M values of lets say patient 1, 6
hours
>>>> sample against 0 hours sample with those from patient 2, also 6
hours
>>>> versus 0 hours where the chips from the two patients were NOT
>>>> normalized together.
>>>>
>>>> -now my question is if someone else has experience in what
samples
>>>> could and should be normalized together with RMA. I saw that ther
are
>>>> (big) differences in the regulation (M) values if i normalize two
>>>> different patients together compared with the values that i
>>>> get when i
>>>> normalize only samples from the same patients together.
>>>>
>>>> thanks in advance
>>>>
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>>
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>
> Naomi S. Altman 814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics 814-863-7114 (fax)
> Penn State University 814-865-1348
(Statistics)
> University Park, PA 16802-2111
>
>
>
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