p.spatial2 {OLIN} | R Documentation |
This function assesses the significance of spatial bias. This is achieved by comparing the observed average values of logged fold-changes within a spot's spatial neighbourhood with an empirical distribution generated by permutation tests. The significance is given by (adjusted) p-values derived in one-sided permutation test.
p.spatial2(object,delta=2,N=-1,av="median",p.adjust.method="none")
object |
object of class marrayRaw or marrayNorm |
delta |
integer determining the size of spot neighbourhoods
((2*delta+1)x(2*delta+1) ). |
N |
number of samples for generation of empirical background distribution |
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 . |
The function p.spatial2.Rd
is basically the same as p.spatial
,
but differs in its input and output formats. Details about the functionality can be found
at p.spatial
.
A list of a two lists of vectors is produced containing the p-values for positive (Pp
)
and negative (Pn
) deviations of
median/mean of M
of the spot's neighbourhood is produced (see example below).
This function will be fused with p.spatial
in future versions using S4-style methods.
Matthias E. Futschik (http://itb.biologie.hu-berlin.de/~futschik)
fdr.int
, sigxy.plot
, p.adjust
,p.spatial
# To run these examples, "un-comment" them! # # LOADING DATA # 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.spatial2(sw,delta=2,N=10000,av="median") # SIGNIFICANCE PLOTS OF ARRAY 1 # sigxy.plot2(sw[,1],P$Pp[[1]],P$Pn[[1]],color.lim=c(-5,5),main="P-value") # SIGNIFICANCE PLOTS OF ARRAY 3 # sigxy.plot2(sw[,3],P$Pp[[3]],P$Pn[[3]],color.lim=c(-5,5),main="P-value")