Hi Andy.
I don't think you've gotten a response on this. Sorry for the delay
-- holidays. Some comments below.
On 31/12/2008, at 1:18 AM, Andy_Paparountas wrote:
>
> Hi all ,
>
> I really find this conversation very interesting. I am trying to
> analyze a set of 3 treatment and 3 control s
Hi all ,
I really find this conversation very interesting. I am trying to
analyze a set of 3 treatment and 3 control samples of MoGeneSt10
array. Thus far with the code pwhite shared I was able to do RMA
Background correction , quantile normalization and got QC , RLE ,
NUSE , density plots.
Q1.
Thanks Peter.
Perhaps you can repeat this comparison after the next release (this
will be very soon!) and split the aroma.affymetrix comparison into:
- aroma.affy.oligo -- with RmaPlm(csN,flavor="oligo")
- aroma.affy.affyPLM -- with flavor="affyPLM" (as you've done already)
Perhaps the best
Dear Mark and Henrik,
I wanted to confirm that your summary was correct regarding the different
flavors for probeset summarization. I downloaded the MAQC HG_U133_Plus_2
array data from the MAQC website:
http://edkb.fda.gov/MAQC/MainStudy/upload/MAQC_AFX_123456_120CELs.zip
I then ran the analysis
Hi Mark,
I ran your code below on the Mouse Gene ST CDF and agree that like the
human array, the probes used by aroma.affymetrix and oligo are
identical, appart from the missing 45 control probesets I mentioned
previously (I listed them below as requested). For the Human array
were the number of
On Dec 3, 8:45 pm, Mark Robinson <[EMAIL PROTECTED]> wrote:
> On 04/12/2008, at 10:17 AM, Henrik Bengtsson wrote:
>
> > So, it all has to do with *how* the log-additive probe-level model is
> > *fitted*, correct?
>
> Correct. Identical linear model, different procedure for fitting.
>
> (as a b
On 04/12/2008, at 10:17 AM, Henrik Bengtsson wrote:
> So, it all has to do with *how* the log-additive probe-level model is
> *fitted*, correct?
Correct. Identical linear model, different procedure for fitting.
(as a bit of an aside ... I think of these things in terms of
influence functio
Hi,
thanks Mark for this.
So, it all has to do with *how* the log-additive probe-level model is
*fitted*, correct? Thus, the model is the same but the algorithms
differ. That gives us some sense of how much variance we get from
using different algorithms regardless of model. Simulation studi
Hi all.
First of all, thanks Peter for 1) doing this testing and 2) for
spelling everything out. I expect to refer people to this thread in
the future, so thanks for that.
Just wanted to add 3 more tidbits of hopefully useful information.
1. I dug a bit into why flavor="oligo" doesn't work
Hi,
thanks for sharing all this.
On Tue, Dec 2, 2008 at 11:54 AM, pwhiteusa <[EMAIL PROTECTED]> wrote:
>
> Hi All,
>
> Here is the exact code I used to analyze Gene ST data for an
> experiment performed with the MoGene-1_0-st-v1 array.
>
> AROMA.AFFYMETRIX
>
> library(aroma.affymetrix)
> cdf <- A
Hi All,
Here is the exact code I used to analyze Gene ST data for an
experiment performed with the MoGene-1_0-st-v1 array.
AROMA.AFFYMETRIX
library(aroma.affymetrix)
cdf <- AffymetrixCdfFile$fromChipType("MoGene-1_0-st-v1",tags="r3")
prefixName <- "Pierson"
cs <- AffymetrixCelSet$byName(prefixN
Hi all.
Just to follow up on these comments here.
'fitPLM' with default parameters in the affyPLM package should give
practically identical results to the 'standard' pipeline (RMA bg corr
+ quantile + fit) within aroma.affymetrix, assuming the underlying
annotation is the same. This was a
Hi.
a comment on RmaPlm and argument 'flavor': The RmaPlm class is only
summarizing the probe signals - normalization etc are done before
RmaPlm. The summarization model is the log-additive model with probe
affinities and chip effects. The 'flavor' argument specifies which
implementation of
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