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)