| findComplexes {apComplex} | R Documentation |
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.
findComplexes(adjMat, simMat = NULL, sensitivity = 0.75, specificity = 0.995, Beta = 0)
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. |
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.
An affiliation matrix representing the estimated protein complex memberships.
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. Submitted.
bhmaxSubgraph,code{LCdelta},mergeComplexes
data(apEX) PCMG2 <- findComplexes(apEX,sensitivity=.7,specificity=.75)