Description Objects from the Class Slots Methods Author(s) References See Also Examples
This S4 class includes methods to do betamixture modelling of functional gene associations given rich phenotyping screens.
Objects of class BetaMixture
can be created from new("BetaMixture",
metric, order, association, model, pheno, partition)
(see the example
below for details).
pheno
:a numeric matrix of rich phenotypes with rows and columns specifying genes and samples, respectively.
metric
:a character value specifying the metric to compute similarity
scores. Currently, 'cosine' and 'correlation' are supported (see
assoScore
for more details).
order
: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.
association
: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.
model
:a character value specifying whether the original (if global
)
or extended (if stratified
) model is used.
partition
: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).
result
:a list storing results from S4 methods of this class.
summary
:a list of summary information for available results.
An overview of methods (More detailed introduction can be found in help for each specific function.):
permNULL
do permutations for input rich phenotyping screens ('pheno'
).
fitNULL
fit the permuted association scores to a beta distribution.
fitBM
fit the functional association scores computed from input screens to a threebeta mixture model.
p2SNR
Translate pvalues to SignaltoNoise Ratios.
SNR2p
Translate SignaltoNoise Ratios to pvalues.
view
view the fitting results (a histogram of the original data and fitted probability density curves) for NULL and real data.
summarize
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.
permNULL
fitNULL
fitBM
view
summarize
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## 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=1e1), verbose=TRUE, gradtol=1e3)
view(bm1, "fitNULL")
view(bm1, "fitBM")
bm1
## End(Not run)

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