I'm normalizing a number of data files obtained from Agilent's miRNA platform. My simple procedure involves using GeneView files (which are processed by Feature Extraction before output) and then filter the miRNA probes by keeping only those with a positive flag in 60% of the samples.
EDIT: As the question wasn't clear, I need to add that the whole time, my data is either in a
data.frame or a
The issue is that of course some "bad" probes for some samples will remain, and they might have 0 or negative intensities. My idea was to set those to NA.
I then proceeded to perform loess normalization (
affy): however, if I log2 the data beforehand (which is part of my workflow), even a single NA on a row will cause the whole row to be NA.
My question is then: should I get rid of those NAs altogether because
normalize.loess is not fit to handle them? Of course I have no problems if I run the normalization in natural scale, but I'd be introducing biases and breaking assumptions.
Thanks in advance.