p.int2: Calculates 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. This is achieved by comparing the observed average values of logged fold-changes within an intensity neighbourhood with an empirical distribution generated by permutation tests. The significance is given by (adjusted) p-values.

1	p.int2(object,delta=50,N=-1,av="median",p.adjust.method="none")

`object`	object of class marrayRaw or marrayNorm
`delta`	integer determining the size of the neighbourhood (`2 * delta+1`).
`N`	number of random samples (of size `2 * delta+1`) used for the generation of empirical distribution. If N is negative, the number of samples 100 times the length of `A`.
`av`	averaging of `M` within neighbourhood by mean or median (default)
`p.adjust.method`	method for adjusting p-values due to multiple testing regime. The available methods are “none”, “bonferroni”, “holm”, “hochberg”, “hommel” and “fdr”. See also `p.adjust`

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

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

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

p.int,fdr.int2, sigint.plot2, p.adjust

# To run these examples, "un-comment" them!
#
# LOADING DATA NOT-NORMALISED
# data(sw)
# CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS
#  For this illustration, N was chosen rather small. For "real" analysis, it should be larger.
# P <- p.int2(sw,delta=50,N=10000,av="median",p.adjust.method="none")
# VISUALISATION OF RESULTS
# sigint.plot2(sw[,1],Sp=P$Pp[[1]],Sn=P$Pn[[1]],c(-5,-5)) # array 1
# sigint.plot2(sw[,3],Sp=P$Pp[[3]],Sn=P$Pn[[3]],c(-5,-5)) # array 3