LCdelta {apComplex}R Documentation

Computes change in LxC measure

Description

Computes the change in the P=LxC measure for AP-MS protein data when two protein complex estimates are combined into one complex.

Usage

LCdelta(comp1, comp2, cMat, dataMat, baitList, simMat, mu, alpha, Beta, wsVal = 2e+07)

Arguments

comp1 Column index in cMat.
comp2 Column index in cMat.
cMat Current protein complex membership estimate affiliation matrix.
dataMat Adjacency matrix of bait-hit data from an AP-MS experiment. Rows correspond to baits and columns to hits.
baitList A vector of the names of the proteins used as baits.
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 dataMat. Higher values in this matrix are interpreted to mean higher similarity for protein pairs.
mu Parameter specification equal to log((1-specificitiy)/specificity).
alpha Parameter specification equal to log(sensitivity/(1-sensitivity)).
Beta Optional additional parameter for the weight to give data in simMat in the logistic regression model.
wsVal Workspace value to be used for computing Fisher's exact test.

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.

When proposing combinations of columns comp1 and comp2 in the PCMG estimate cMat, the proposal is accepted if the output from LCdelta is greater than zero.

Value

The numeric value of the change in P=LxC when columns comp1 and comp2 in cMat are combined into one column.

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,mergeComplexes,findComplexes

Examples


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


[Package apComplex version 1.4.0 Index]