Description Usage Arguments Details Value 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.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 ( |

`N` |
number of random samples (of size |

`av` |
averaging of |

`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 |

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`

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 \code{M}*
with the observed distribution of *median/mean of \code{M}*,
the independence of `M`

and `A`

is assessed. If `M`

is independent of `A`

, the empirical distribution
of *median/mean of \code{M}* can be expected to be symmetrically
distributed around its mean value. To assess the significance of observing positive deviations of
the p-values are used. It indicates the expected proportion of neighbourhoods with larger
*median/mean of \code{M}* than the actual one based on the empirical distribution of
*median/mean of \code{M}*. The minimal p-value is set to `1/N`

.
Correspondingly, the significance
of observing negative deviations of *median/mean of \code{M}* can be determined.
Since this assessment of significance involves multiple testing, an adjustment of the p-values might be advisable.

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

) and negative (`Pn`

) deviations of
*median/mean of \code{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`

.

The same functionality but with our input and output formats is offered by `p.int2`

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

`p.int2`

,`fdr.int`

, `sigint.plot`

, `p.adjust`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# 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))
``` |

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