This S4 class includes methods to do beta-mixture modelling of functional gene associations given rich phenotyping screens.
Objects of class
BetaMixture can be created from
metric, order, association, model, pheno, partition) (see the example
below for details).
a numeric matrix of rich phenotypes with rows and columns specifying genes and samples, respectively.
a character value specifying the metric to compute similarity
scores. Currently, 'cosine' and 'correlation' are supported (see
assoScore for more details).
a numeric value specifying the order of the similarity score to
be computed. Only 1 and 2 is supported for the current version.
The first order (when
order=1) similarities are used for
quatification of the strength of functional associations between
genes, whilst the second order (when
code=2) ones are employed
to compute the strength of modularity between genes.
a numeric vector providing all association scores between genes.
This can be useful when
pheno is not available or the user
has a different way to compute functional associations.
a character value specifying whether the original (if
or extended (if
stratified) model is used.
a numeric of gene partition labels (e.g. c(rep(1, 100), rep(2, 20), rep(3, 80)) is a valid vector of partition labels for a vector of associations falling into three categories of interaction types 1, 2 and 3).
a list storing results from S4 methods of this class.
a list of summary information for available results.
An overview of methods (More detailed introduction can be found in help for each specific function.):
do permutations for input rich phenotyping screens (
fit the permuted association scores to a beta distribution.
fit the functional association scores computed from input screens to a three-beta mixture model.
Translate p-values to Signal-to-Noise Ratios.
Translate Signal-to-Noise Ratios to p-values.
view the fitting results (a histogram of the original data and fitted probability density curves) for NULL and real data.
summarize results including input data and parameters, NULL fitting and beta mixture fitting.
Xin Wang [email protected]
Xin Wang, Mauro Castro, Klaas W. Mulder and Florian Markowetz, Posterior association networks and enriched functional gene modules inferred from rich phenotypic perturbation screens, in preparation.
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## Not run: data(Bakal2007) bm1<-new("BetaMixture", pheno=Bakal2007, metric="cosine", model="global", order=1) bm1<-fitNULL(bm1, nPerm=10, thetaNULL=c(alphaNULL=4, betaNULL=4), sumMethod="median", permMethod="all", verbose=TRUE) bm1<-fitBM(bm1, para=list(zInit=NULL, thetaInit=c(alphaNeg=2, betaNeg=4, alphaNULL=bm1@result$fitNULL$thetaNULL[["alphaNULL"]], betaNULL=bm1@result$fitNULL$thetaNULL[["betaNULL"]], alphaPos=4, betaPos=2), gamma=NULL), ctrl=list(fitNULL=FALSE, tol=1e-1), verbose=TRUE, gradtol=1e-3) view(bm1, "fitNULL") view(bm1, "fitBM") bm1 ## End(Not run)
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