Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/mergeComplexes.R
Repeatedly applies the function LCdelta
to make combinations of columns in the affiliation matrix representing the protein complex membership graph (PCMG) for APMS data.
1  mergeComplexes(bhmax,adjMat,VBs=NULL,VPs=NULL,simMat=NULL,sensitivity=.75,specificity=.995,Beta=0,commonFrac=2/3,wsVal = 2e7)

bhmax 
Initial complex estimates coming from bhmaxSubgraph 
adjMat 
Adjacency matrix of baithit data from an APMS experiment. Rows correspond to baits and columns to hits. 
VBs 

VPs 

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 
sensitivity 
Believed sensitivity of APMS technology. 
specificity 
Believed specificity of APMS technology. 
Beta 
Optional additional parameter for the weight to give data
in 
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 to the workspace in the call to fisher.test. 
The local modeling algorithm for APMS data described by Scholtens and
Gentleman (2004) and Scholtens, Vidal, and Gentleman (2005) uses a
twocomponent measure of protein complex estimate quality, namely P=LxC.
Columns in cMat
represent individual complex estimates. The algorithm
works by starting with a maximal BHcomplete subgraph estimate of cMat
,
and then improves the estimate by combining complexes such that P=LxC
increases.
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 highthroughput proteinprotein 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, 35483557 (2005).
1 2 3  data(apEX)
PCMG0 < bhmaxSubgraph(apEX)
PCMG1 < mergeComplexes(PCMG0,apEX,sensitivity=.7,specificity=.75)

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