R: Simulate the distribution of maximal minimal distances in a...

distances {SARP.compo}

R Documentation

Simulate the distribution of maximal minimal distances in a random graph

Description

This function simulates the distribution of the maximum of the minimal distances between nodes of a random graph, for a given cut-off threshold.

Usage

distrib.distances( n.genes,
                   taille.groupes = c( 10, 10 ), masque,
                   me.composition = 0, cv.composition = 1, en.log = TRUE,
                   seuil.p = 0.05,
                   B = 3000, conf.level = 0.95,
                   f.p = student.fpc, frm = R ~ Groupe,
                   n.coeurs = 1 )

Arguments

`conf.level`	The confidence level for the exact confidence intervals of estimated probabilities of maximal minimal distances in the graph.
`n.genes`	Number of components in the system (of nodes in the total graph). Ignored if `me.composition` is a matrix.
`me.composition`	The expected median quantity of each component, in the log scale. Can be either a single value, used for two conditions and `n.genes` components (hence, assuming the null hypothesis that no change occurs), or a matrix with one row by experimental condition and one column by component.
`cv.composition`	The expected coefficient of variation of the quantified amounts. Should be either a single value, that will be used for all components and all conditions, or a matrix with the same structure than `me.composition`: one row for each condition, one column for each component, in the same order and with the same names. Coefficients of variations are expected in the amount scale, in raw form (that is, give 0.2 for a 20% coefficient of variation)

`en.log`	If `TRUE`, the values in the matrices are given in the log scale.
`taille.groupes`	The sample size for each condition. Unused if `masque` is given. If a single value, it will be used for all conditions. Otherwise, should have the same length that the number of rows in the provided matrices.
`masque`	A data.frame that will give the dataset design for a given experiment. Should contain at least one column containing the names of the conditions, with values being in the conditions names in `composition`. If not provided, it is generated from `taille.groupes` as a single column named ‘Condition’.
`f.p`, `frm`	The function used to analyse the dataset, and its parameter. See `creer.Mp` for details.
`seuil.p`	The p-value cut-off to be used when creating the graph. Should be between 0 and 1. See `grf.Mp` for details.
`B`	The number of simulations to be done.
`n.coeurs`	The number of CPU cores to use to parallelize the simulation.

Details

In an undirected graph, minimal distance between two nodes is the minimal number of edges to cross to go from one node to the other. The maximal minimal distance is the largest of all possible minimal distances in a given graph.

The function simulates the distribution of the maximal minimal distance in a graph whose edges were removed according to the specified p-value cut-off. To avoid infinite distances, these distances are computed in the largest connected component of the graph.

In the observed graph, nodes that are at a largest minimal distance than probable maximal minimal distances may signal components belonging to different sets, that could not be disconnected because of some nodes having intermediate changes.

Value

A 4-columns data.frame, with additional attributes giving the number of simulations (Nombre.simulations) and their results (Tirages). The first column contains the maximal minimal distances, the second contains their observed frequencies in the simulated datasets, the third and fourth contain the limits of the confidence interval of the corresponding probability.

Confidence intervals are exacts, using the Clopper-Pearson method.

Author(s)

Emmanuel Curis (emmanuel.curis@parisdescartes.fr)