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
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.
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
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 -
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 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
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
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
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
