p.spatial {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.spatial(X,delta=2,N=-1,av="median",p.adjust.method="none")

`X` |
matrix of logged fold changes |

`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.spatial`

assesses the significance of spatial bias using an one-sided random
permutation test.
The null hypothesis states random spotting i.e. the independence of log ratio `M`

and spot location. First, a neighbourhood of a spot is defined by a two dimensional square window
of chosen size ((2*delta+1)x(2*delta+1)). Next, a test statistic is defined by calculating
the *median* or *mean* of `M`

(*bar{M}*) for `N`

random samples
of size ((2*delta+1)x(2*delta+1)). Note that this scheme defines a sampling with replacement
procedure whereas sampling without replacement is used for `fdr.spatial`

.
Comparing the empirical distribution of *median/mean of M*
with the observed distribution of

`M`

`M`

and spot location
can be assessed. If `M`

is independent of spot's location,
the empirical distribution can be expected to be
distributed around its mean value. To assess the significance of observing positive deviations of
`M`

`1/N`

.
Correspondingly, the significance
of observing negative deviations of `M`

A list of vectors containing the p-values for positive (`Pp`

)
and negative (`Pn`

) deviations of
*median/mean of M* of the spot's neighbourhood is produced.

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

# To run these examples, "un-comment" them! # # LOADING DATA # data(sw) # M <- v2m(maM(sw)[,1],Ngc=maNgc(sw),Ngr=maNgr(sw), # Nsc=maNsc(sw),Nsr=maNsr(sw),main="MXY plot of SW-array 1") # # CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS # For this illustration, N was chosen rather small. For "real" analysis, it should be larger. # P <- p.spatial(M,delta=2,N=10000,av="median") # sigxy.plot(P$Pp,P$Pn,color.lim=c(-5,5),main="FDR") # LOADING NORMALISED DATA # data(sw.olin) # M <- v2m(maM(sw.olin)[,1],Ngc=maNgc(sw.olin),Ngr=maNgr(sw.olin), # Nsc=maNsc(sw.olin),Nsr=maNsr(sw.olin),main="MXY plot of SW-array 1") # CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS # P <- p.spatial(M,delta=2,N=10000,av="median") # VISUALISATION OF RESULTS # sigxy.plot(P$Pp,P$Pn,color.lim=c(-5,5),main="FDR")

[Package *OLIN* version 1.3.2 Index]