[BioC] Plot signal distribution in miRNA arrays

[Davis, Wade]

Hi Andrea,
As far as plotting, you would plot it as you would any other microarray data. 
Since you are interested in distribution across genes in a single array, then some subsetting of your expression matrix like hist(mydata[,1]) would give you a histogram of the first sample (assuming samples are stored column-wise as is typical). Or you could do plot(density()) instead of hist().

But my primary reason for responding to your question is to ask why you would assume the distribution would or should be normal across mirna's in a GIVEN sample. It is hard for me to think why it should be (biologically), and there is no statistical requirement for it to be. (Quantile normalization won't impose normality, but the log2 transform will make the distribution more symmetric.) 

I have attached some plots from an Affy mirna (2.0) mouse experiment that I happened to have open in R right now. The QC pdf contains plots before normalizing, and the other is after normalizing. (Note that it is really not necessary to plot each density after quantile normalization, they must look at the same by definition. I just did it since you asked about plotting each one separately). Notice how the distribution is long tailed.

BTW, here is sample code for the density plot:

par(mfrow=c(3,2),pty="s")
for(i in 1:6){
plot(density(exprs(mirna.norm_mouse)[,i]))
}


Wade




-----Original Message-----
From: andrea.grilli at ior.it [mailto:andrea.grilli at ior.it] 
Sent: Wednesday, August 03, 2011 4:53 AM
To: bioconductor at r-project.org
Subject: [BioC] Plot signal distribution in miRNA arrays

Hi to all,

I would like to plot signal distribution from miRNA array experiment,  
one only plot for all arrays. Any suggestion on how to perform it? Is  
there some easy way to check how this data fit a normal distribution  
(e.g. with K-S test)?

Data comes from Agilent Human miRNA v.2, normalized with quantile  
method and log2 transformed.

Thanks in advance,
Andrea


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