fdr.int {OLIN} | R Documentation |

This function assesses the significance of intensity-dependent bias by an one-sided random permutation test. The observed average values of logged fold-changes within an intensity neighbourhood are compared to an empirical distribution generated by random permutation. The significance is given by the false discovery rate.

fdr.int(A,M,delta=50,N=100,av="median")

`A` |
vector of average logged spot intensity |

`M` |
vector of logged fold changes |

`delta` |
integer determining the size of the neighbourhood. The actual window size is
(`2 * delta+1` ). |

`N` |
number of random permutations performed for generation of empirical distribution |

`av` |
averaging of `M` within neighbourhood by mean or median (default) |

The function `fdr.int`

assesses significance of intensity-dependent bias using a one-sided random 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, a test statistic is defined by calculating the *median* or *mean* of `M`

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

). An empirical distribution of the
test statistic is produced by calculating for `N`

random intensity orders of spots.
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`

`s`

is the number of neighbourhoods with
`M`

)> c`T`

is the total number of neighbourhoods in the original data.
Varying threshold `log10(FDR)`

is plotted by `sigint.plot`

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

`NA`

.
A list of vector containing the false discovery rates for positive (`FDRp`

) and negative (`FDRn`

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

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

`p.int`

, `fdr.spatial`

, `sigint.plot`

# 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. # FDR <- fdr.int(maA(sw)[,1],maM(sw)[,1],delta=50,N=10,av="median") # VISUALISATION OF RESULTS # sigint.plot(maA(sw)[,1],maM(sw)[,1],FDR$FDRp,FDR$FDRn,c(-5,-5)) # LOADING NORMALISED DATA # data(sw.olin) # CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS # FDR <- fdr.int(maA(sw.olin)[,1],maM(sw.olin)[,1],delta=50,N=10,av="median") # VISUALISATION OF RESULTS # sigint.plot(maA(sw.olin)[,1],maM(sw.olin)[,1],FDR$FDRp,FDR$FDRn,c(-5,-5))

[Package *OLIN* version 1.3.2 Index]