findComplexes: Estimate a Protein Complex Membership Graph (PCMG) using...
In apComplex: Estimate protein complex membership using AP-MS protein data

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1 2	findComplexes(adjMat,VBs=NULL,VPs=NULL,simMat=NULL,sensitivity=.75,specificity=.995,Beta=0,commonFrac=2/3,wsVal = 2e7)

`adjMat`	Adjacency matrix of bait-hit data from an AP-MS experiment. Rows correspond to baits and columns to hits.
`VBs`	`VBs` is an optional vector of viable baits.
`VPs`	`VPs` is an optional vector of viable prey.
`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.
`commonFrac`	This is the fraction of baits that need to be overlapping for a complex combination to be considered.
`wsVal`	A numeric. This is the value assigned as the work-space in the call to fisher.test

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.

If VBs and/or VPs are not specified, then by default VBs will be assigned the set of baits that detect at least one prey and VPs the set of prey that are detected by at least one bait.

By default commonFrac is set relatively high at 2/3. This means that some potentially reasonable complex combinations could be missed. For smaller data sets, users may consider decreasing the fraction. For larger data sets, this may cause a large increase in computation time.

A list of character vectors containing the names of the proteins in the estimated complexes.

Denise Scholtens

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. Bioinformatics 21, 3548-3557 (2005).

bhmaxSubgraph,mergeComplexes