scoreFunction


Model from the beta-uniform mixture fitting.



Numeric constant, from the false discovery rate a p-value threshold is calculated. P-values below this threshold
are considered to be significant and will score positively, p-values a bove the threshold are supposed to arise
from the null model. The FDR can be used to control the size of the maximum scoring subnetwork, by zooming in and out 
in the same region.



The function calculates a score for each gene with a given FDR from the fitted beta-uniform mixture model. 


This package provides functions for the integrated
analysis of protein-protein interaction networks and the
detection of functional modules. Different datasets can be
integrated into the network by assigning p-values of
statistical tests to the nodes of the network. E.g. p-values
obtained from the differential expression of the genes from an
Affymetrix array are assigned to the nodes of the network. By
fitting a beta-uniform mixture model and calculating scores
from the p-values, overall scores of network regions can be
calculated and an integer linear programming algorithm
identifies the maximum scoring subnetwork.

Marcus Dittrich

BioNet

Routines for the functional analysis of biological networks

scoreFunction function

Model from the beta-uniform mixture fitting.

Numeric constant, from the false discovery rate a p-value threshold is calculated. P-values below this threshold
are considered to be significant and will score positively, p-values a bove the threshold are supposed to arise
from the null model. The FDR can be used to control the size of the maximum scoring subnetwork, by zooming in and out 
in the same region.

The function calculates a score for each gene with a given FDR from the fitted beta-uniform mixture model.

scoreFunction: Scoring function for p-values

Description

Usage

Arguments

Value

References

Examples