[BioC] One-Color Agilent miRNA microarrays
Gordon K Smyth
smyth at wehi.EDU.AU
Sun Aug 5 06:42:24 CEST 2007
On Fri, August 3, 2007 2:40 am, Francesco Favero wrote:
> 2007/8/2, Gordon K Smyth <smyth at wehi.edu.au>:
>> > Date: Mon, 30 Jul 2007 12:39:19 +0200
>> > From: "Francesco Favero" <favero.francesco at gmail.com>
>> > Subject: [BioC] One-Color Agilent miRNA microarrays
>> > fit <- lmFit(MA,design,ndups=20,spacing=1,correlation=dupcor$consensus)
>> >> Errore in chol(V) : il minore principale di ordine 2 non Ã¨ definito
>> >> positivo
>> > (Sorry for the error in Italian...anyway...)
>> > It works if I don't use ndups, but I need this...
>> What sort of object is MA? Is it just a matrix, or is it a MAList?
> It's just a list of log2 intensities even if is the output of
> normalizeBetweenArrays function.
I suspect you mean that it is a 'matrix'. It cannot be of class 'list'. Try
> In any data analysis, you should check you results at every step. For
>> example, have you looked at
>> your normalized data? What is the value of corfit$consensus?
> Yes, I have a corfit$consensus of 1.
This means your data is degenerate in some way, so you cannot use duplicateCorrelation() without
reworking your data.
> I've seen it's for this I can't use
> I use ndups=20 so I expect to have some differences between different spot
> for the same genes.. in fact atanh.correlations is good for some gene, but
> for a lot is Inf or NA, maybe this is because only few spots have a good
> Intensity, in average the intensity is very low. this is a problem for miRNA
> in cell-lines. Anyway I think it' possible I still have to work on
> Is it so wrong go around all those problems using lm.series function?
Using lm.series is the same as using correlation=0 to lmFit(). Since your estimated correlation
is large, it would probably be better to average over your duplicates. This can be done by using
avedups() before using lmFit().
More information about the Bioconductor