mergeComplexes {apComplex}R Documentation

Iteratively combine columns in initial PCMG estimate

Description

Repeatedly applies the function LCdelta to make combinations of columns in the affiliation matrix representing the protein complex membership graph (PCMG) for AP-MS data.

Usage

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

Arguments

PCMG Current PCMG estimate
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

The local modeling algorithm for AP-MS data described by Scholtens and Gentleman (2004) and Scholtens, Vidal, and Gentleman (submitted) uses a two-component measure of protein complex estimate quality, namely P=LxC. Columns in cMat represent individual complex estimates. The algorithm works by starting with a maximal BH-complete subgraph estimate of cMat, and then improves the estimate by combining columns such that P=LxC increases.

When proposing combinations of columns comp1 and comp2 in cMat, the proposal is accepted if the output from LCdelta (the log of LxC) is greater than zero. mergeComplexes performs all column combinations until no more combinations result in an output from LCdelta greater than zero.

Value

An affiliation matrix representing the estimated PCMG. The number of rows and the row labels of the matrix will be the same as adjMat. The number of columns will be less than or equal to the number of columns in adjMat.

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

LCdelta,bhmaxSubgraph,findComplexes

Examples


data(apEX)
PCMG0 <- bhmaxSubgraph(apEX)
PCMG1 <- mergeComplexes(PCMG0,apEX,sensitivity=.7,specificity=.75)


[Package apComplex version 1.4.0 Index]