Question: affyPLM interpretation

0

Arne.Muller@aventis.com •

**620**wrote:Hi All,
I've some question regarding the affyPLM package. Maybe you can give
some
hints ... .
fitPLM is *not* a normalisation method, is it? I mean, it performs a
normalisation such as RMA background correction and e.g. quantile
cross-chip
normalisation, but it doesn't summarize the probes that belong to one
probe
set into a single expression value. Instead, a robust (?) linear model
is fit
through *all* the probes of each probeset on each chip. Is this kind
of
interpretation correct?
How do I then interpret the coefficients of the PlmSet object? Say
I've a 40
chips for measuring gene expression after 4h and 24h treatment with a
drug
with doses 0mM, 0.1mM, 0.25mM, 0.5mM and 1.0mM (this is a typical
design for
me).
I'd create factors
time <- factor(c('04h','24h'))
dose <- factor(c('0mM', '0.1mM', '0.25mM', '0.5mM', '1.0mM'))
and then do the fit with an intercept
plm <- fitPLM(affybatch, model = PM ~ probes + dose + time)
I'm not sure what to do with the coefficients of the result, what do
they
tell me?
A while ago I've analysed my data with linear model and anova in a way
like
foreach gene in genes:
mylm <- lm(intensity ~ dose + time,
data=all_chips_dataframe_for_gene)
myanova <- anova(lm)
Then I've extracted the p-values for the dose and time factor for each
gene
to see what's differentially regulated ... .
In the example above I'm using the linear model for an anova - which
makes
sense to me, and again the coefficients of "lm" wouldn' tell me much.
Could I
use the the PlmSet for an anova, too?
thanks for your comments
+kind regards,
Arne