Dear All,

 

I am trying to do classification on 92 Affymetrix data-set using Random
Forest.   I followed the examples on "Machine Learning Lab" June 11,
2004.  I have problem to understand some of the codes for the
non-specific filtering step.  

 

Here are the codes in the paper:

 

*       library(genefilter)

*       f1 <- pOverA(0.25, log2(200))

*       f2 <- function(x) (IQR(x) > 0.5)

*       ff <- filterfun(f1,f2)

*       selected <- genefilter(eset, ff)

*       sum(selected)

 

 

For "pOverA(0.25, log2(200))", is this means if 25% of samples'
expression values from same gene > log2(200), then it return true?

 

For "f2 <- function(x) (IQR(x) > 0.5)", I tried the help page, but still
don't understand.  I assume it have something to do with filter genes
show little variation across samples.  

 

For "ff <- filterfun(f1,f2)", the help page said "the function returns
FALSE when the first filter function returns FALSE otherwise it return
TRUE".  So why we need f2 if is decided by first function, which is f1
here?

 

Could someone explain this for me?

 

Thanks in advance,

 

Weiyin

 

 


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