LCdelta {apComplex} | R Documentation |
Computes the change in the P=LxC measure for AP-MS protein data when two protein complex estimates are combined into one complex.
LCdelta(comp1, comp2, cMat, dataMat, baitList, simMat, mu, alpha, Beta, wsVal = 2e+07)
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. |
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.
The numeric value of the change in P=LxC when columns comp1
and comp2
in cMat
are combined into one column.
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
,findComplexes
data(apEX) PCMG0 <- bhmaxSubgraph(apEX) PCMG1 <- mergeComplexes(PCMG0,apEX,sensitivity=.7,specificity=.75)