findComplexes {apComplex}R Documentation

Estimate a Protein Complex Membership Graph (PCMG) using protein complex comembership data from AP-MS technology

Description

Performs all steps in the local modeling algorithm described by Scholtens and Gentleman (2004) and Scholtens, Vidal, and Gentleman (submitted), beginning with an adjacency matrix recording bait-hit AP-MS data.

Usage

findComplexes(adjMat, simMat = NULL, sensitivity = 0.75, specificity = 0.995, Beta = 0)

Arguments

adjMat Adjacency matrix of bait-hit data from an AP-MS experiment. Rows correspond to baits and columns to hits.
simMat An optional square matrix with entries between 0 and 1. Rows and columns correspond to the proteins in the experiment, and should be reported in the same order as the columns of adjMat. Higher values in this matrix are interpreted to mean higher similarity for protein pairs.
sensitivity Believed sensitivity of AP-MS technology.
specificity Believed specificity of AP-MS technology.
Beta Optional additional parameter for the weight to give data in simMat in the logistic regression model.

Details

findComplexes performs all steps in the complex estimation algorithm using the apComplex package functions bhmaxSubgraph, LCdelta, and mergeComplexes. These steps can also be performed separately by the user.

Value

An affiliation matrix representing the estimated protein complex memberships.

Author(s)

Denise Scholtens

References

Scholtens D and Gentleman R. Making sense of high-throughput protein-protein interaction data. Statistical Applications in Genetics and Molecular Biology 3, Article 39 (2004).

Scholtens D, Vidal M, and Gentleman R. Local modeling of global interactome networks. Submitted.

See Also

bhmaxSubgraph,code{LCdelta},mergeComplexes

Examples


data(apEX)
PCMG2 <- findComplexes(apEX,sensitivity=.7,specificity=.75)


[Package apComplex version 1.4.0 Index]