p.int {OLIN} | R Documentation |

This function assess 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.

p.int(A,M,delta=50,N=-1,av="median",p.adjust.method="none")

`A` |
vector of average logged spot intensity |

`M` |
vector of logged fold changes |

`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` |

The function `p.int`

assesses the significance of intensity-dependent bias using a permutation test.
The null hypothesis states the independence of A and M. To test if `M`

depends on `A`

,
spots are ordered with respect to A. This defines a neighbourhood of spots with similar A for each spot.
Next, the test statistic is the *median* or *mean* of `M`

(*bar{M}*) within
a spot's intensity neighbourhood of chosen size (`2 *delta+1`

). The empirical distribution of the
this statistic is then generated based on `N`

random samples (with replacement).
(Note that sampling without replacement is used for `fdr.int`

. Also note, that different meaning of argument `N`

in `p.int`

and `fdr.int`

. The argument `N`

in `p.int`

is the number fo independent samples (of size `2 *delta+1`

)
derived from the original distribution. The argument `N`

in `fdr.int`

states how many times the original distribution
is randomised and the permutated distribution is used for generating the empirical distribution.)
Comparing this empirical distribution of *median/mean of M*
with the observed distribution of

`M`

`M`

and `A`

is assessed. If `M`

is independent of `A`

, the empirical distribution
of `M`

`M`

`M`

`1/N`

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

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

) and negative (`Pn`

) deviations of
*median/mean of M* of the spot's neighbourhood is produced. Values corresponding to spots
within an interval of

`delta`

at the lower or upper end of the `A`

-scale are set to `NA`

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

`fdr.int`

, `sigint.plot`

, `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.int(maA(sw)[,1],maM(sw)[,1],delta=50,N=10000,av="median",p.adjust.method="none") # VISUALISATION OF RESULTS # sigint.plot(maA(sw)[,1],maM(sw)[,1],Sp=P$Pp,Sn=P$Pn,c(-5,-5)) # LOADING NORMALISED DATA # data(sw.olin) # CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS # P <- p.int(maA(sw.olin)[,1],maM(sw.olin)[,1],delta=50,N=10000,av="median",p.adjust.method="none") # VISUALISATION OF RESULTS # sigint.plot(maA(sw.olin)[,1],maM(sw.olin)[,1],Sp=P$Pp,Sn=P$Pn,c(-5,-5))

[Package *OLIN* version 1.3.2 Index]