fdr.int2: Assessment of the significance of intensity-dependent bias
In OLIN: Optimized local intensity-dependent normalisation of two-color microarrays

Description Usage Arguments Details Note Author(s) See Also Examples

This function assesses the significance of intensity-dependent bias by an one-sided random permutation test. The observed average values of logged fold-changes within an intensity neighbourhood are compared to an empirical distribution generated by random permutation. The significance is given by the false discovery rate.

1	fdr.int2(object,delta=50,N=100,av="median")

`object`	object of class marrayRaw or marrayNorm
`delta`	integer determining the size of the neighbourhood. The actual window size is (`2 * delta+1`).
`N`	number of random permutations performed for generation of empirical distribution
`av`	averaging of `M` within neighbourhood by mean or median (default)

This function fdr.int2 is basically the same as fdr.int except for differences in their in- and output format. For the details about the functionality, see fdr.int.

This function will be merged with fdr.int in future versions.

Matthias E. Futschik (http://itb.biologie.hu-berlin.de/~futschik)

fdr.int, p.int2, sigint.plot2

# To run these examples, delete the comment signs (#) in front of the commands.
#
# LOADING DATA NOT-NORMALISED
# data(sw)
# CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS
# For this example, N was chosen rather small. For "real" analysis, it should be larger.
# FDR <- fdr.int2(sw,delta=50,N=10,av="median")
# VISUALISATION OF RESULTS
# sigint.plot2(sw[,1],FDR$FDRp[[1]],FDR$FDRn[[1]],c(-5,-5)) # array 1
# sigint.plot2(sw[,4],FDR$FDRp[[4]],FDR$FDRn[[4]],c(-5,-5)) # array 4