thank you for your help.
My experimental design is a reference design with three classes and
array per class (a total of 15 arrays).
We do not have technical replicates, but each array refers to a
copy compared with a pool of the five wild type (we use dual-color
microarrays). One class is constituted from 5 copies of Hela G1 line
transformed with a Wild type mutation of BRCA1. The second one is
constituted from 5 copies of Hela G1 cell line transformed with a
pathological mutation of BRCA1 and the last one is constituted from 5
copies of Hela G1 line cells transformed with another mutation of
are interested in comparing the two groups of transformet cells with
types of mutation respet to Wild Type mutation.
We chose this design and this number of copies because the variance
on a cell line is lower respert to that observed on animals (i.e.
Are, in you opinion, 5 arrays per class enought to use arrayWeights()?
About weighting array spot-to-spot, what is your opinion?
I am not sure I have to consider as good, and then usefull to fit
model, saturated spots. In all measurement process this kind of values
discarded because are outside the range of reliability of the
used to permorm the measurement.
If GenePix flags as NotFound a spot because the level of hybridization
not enought to consider reliable the level of hybridization, why
use this signal to fit my model?
And concerning weighting spots before or after they have been
The result change completely!
I am not much persuader about using unreliable spots and a lot
about the workflow to be followed.
Could you, or anyone else, help me?
Thank you so much
----- Original Message -----
From: "Matt Ritchie" <firstname.lastname@example.org>
To: "Jenny Drnevich" <drnevich at="" illinois.edu="">; "Erika Melissari"
<erika.melissari at="" bioclinica.unipi.it="">
Cc: <bioconductor at="" stat.math.ethz.ch="">
Sent: Thursday, February 19, 2009 05:12 AM
Subject: Re: [BioC] ...another question about using weights on
> Dear Jenny and Erika,
> Regarding the question on array weights, so long as you have enough
> to fit the linear model to the means, you will be able to estimate
> variance factors using arrayWeights(). The example given on the
> arrayWeights help page uses the methodology on a set of 6 arrays
> replicates per group.
> And yes, spot and array weights can be combined in the analysis by
> multiplying them together. Make sure that what you are multiplying
> matrices of the same dimension though - the output of arrayWeights
> vector, so you will want to run asMatrixWeights() on this vector
> multiplying with the spot weights.
> Best wishes,
>>Filtering spots on each array individually has been addressed
>>times on the list, and the general consensus is to only do it in
>>circumstances, such as when you have manually flagged spots that are
>>scratches, dust spots, e.g., where the reported value has ABSOLUTELY
>>RELATIONSHIP to whatever the real value might have been. Spots with
>>SNR, auto-flagged by GenePix as "missing", or saturated spots all
>>values that are approximations of what the real value is, even if
>>aren't as precise because they are outside the measurement abilities
>>the scan. As I tell my students - zeros are REAL data points - would
>>throw them out in other scientific measurements? NO. It is fine to
>>out a spot that fails to meet your criteria on ALL arrays, like the
>>I'm not sure about the array quality weights... the example uses 10
>>replicates per group, which is probably a fine number to use to
>>which arrays aren't as much alike as the others, but I'm not sure if
>>good to use when you only have 3 replicates. Anyone care to comment
>>At 05:49 AM 2/17/2009, Erika Melissari wrote:
>>>I have found discordant opinions among Bioconductor email regarding
>>>use of quality weights on microarray analysis and I woul like to
>>>understand with clarity what to do before starting the statistical
>>>analysis of my last experiment.
>>>I use LIMMA to perform statistical analysis of microarray
>>>Usually, I assign a weight to all the spots of my experiment by
>>>read.maimages() this wt.fun:
>>>#to exclude spots with SNR<3 on both channels
>>>snrok <- !(x[,"SNR 635"] < threshold & x[,"SNR 532"] < threshold );
>>>#to include only genes and not control spots (I use Agilent
>>>spotok <- (x[,"ControlType"] == "false");
>>>#to exclude spots with flag "bad" by GenePix Pro 6
>>>flagok <- (x[,"Flags"] >= 0);
>>>#to exclude spot saturated
>>>satok <- !(x[,"F635 % Sat."] > 10 | x[,"F532 % Sat."] > 10 );
>>>spot <- (snrok & spotok & flagok & satok);
>>>In my opinion it is right to exclude spot saturated (because its
>>>intensity value is not reliable). Is it wrong?
>>>I have a doubt about excluding spot with low SNR, because in my
>>>experiment I should exclude for low SNR about 60% of 45000 spots
and I am
>>>worried about the robustness of statistical analysis evalued only
>>>of the genes.
>>>Should I exclude this spot?
>>>Before or after normalization?
>>>Should I normalize all the spots and then, on the normalized value,
>>>the SNR quality filter to exclude normalized spots with low SNR
>>>subsequent statistical analysis?
>>>I would like to use arrayWeights() from LIMMA and combine spot
>>>weights and array quality weights. Is it right to multiply the spot
>>>weight matrix by array quality vector?
>>>thank you very much for any help on this complicate question.