prior: Calculate a structure prior. Usually called internally.

In ddepn: Dynamic Deterministic Effects Propagation Networks: Infer signalling networks for timecourse RPPA data.

Description

Provides three types of structure priors. laplaceinhib and laplace penalise the difference between the actual network and a reference network. scalefree penalises high node degrees.

Usage

prior(phi, lambda = NULL, B = NULL, Z = NULL,
   gam = NULL, it = NULL, K = NULL, priortype = "laplaceinhib")
   
calcpr(lambda, B, phi, gam)

Arguments

phi	The candidate network.
lambda	Laplace prior hyperparameter describing the prior influence strength.
B	Laplace prior probability matrix.
Z	Laplace prior normalisation factor for the prior. (Not used at the moment.)
gam	Scalefree prior degree distribution coefficient: P(k) ~ k^gam or exponent for difference term in laplaceinhib prior.
K	Scale-free prior scaling factor/Strength
it	Scale-free prior number of iterations for prior sampling.
priortype	String. Either uniform, laplaceinhib, laplace or scalefree. uniform assumes uniform prior network distributions. laplaceinhib calculates the difference between the candidate network and a reference matrix containing edge probabilites, while both edge types (activation and inhibition) are included. laplace is the same, except for ignoring the edge type. scalefree calculates a probability of a network following a scale free network architecture. See the references for a detailed description of the priors.

Details

For the laplaceinhib and laplace prior types, the matrix B is of central importance. The matrix has the same dimensions as the network to be inferred, each entry corresponding to a confidence in the existence of the respective edge. This confidence can be aquired by using external pathway sources, e.g. the KEGG database. See the vignette for a description of how to get the prior matrices. No matter how the confidence scores are obtained, there are two options, either use the laplaceinhib prior type, in which knowledge about the type of the edges is present in the external pathway source. Each confidence score for an edge, that is found as inhibiting edge in the reference pathways, is multiplied by -1 to obtain a negative value for the inhibiting edges. The entries of the prior matrix B thus lie in the interval [-1;1], where -1 means strong confidence that an edge is an inhibition, 1 means strong confidence that the edge is an activation and 0 means that nothing is known about the presence or type of the edge.

If no information on the type of the edges is available in the external data source, priortype="laplace" should be used, where the edge confidence ranges in the interval [0;1], where 1 means strong confidence that the edge is present and 0 means that nothing is known about the presence of the edge.

Argument gam is used either as exponent in the scalefree prior, as it is described in the reference, or in laplaceinhib and laplace as exponent in the following formula:
P(phi_{ij}|lambda,gam,B) = \frac{1}{2 \cdot lambda} exp(\frac{-|phi_{ij}-B_{ij}|^{gam}}{lambda})
It controls how strong the differences between an inferred edge and the probability for seeing this edge in a reference set of networks are to be weighted. Defaults to 1, if omitted. The prior curve rapidly decayes with increasing difference |phi_{ij}-B_{ij}|, while for gam larger than 1, the prior curve is changed to an s-shaped curve with a plateau at the upper bound of P(phi_{ij}|lambda,gam,B) and an exponential decay when a certain threshold of |phi_{ij}-B_{ij}| is reached.

See also the references for a description of the priors.

The calcpr function is a helper that calculates the laplace prior probability.

Value

Returns a double for the prior probability of network structure phi.

Note

TODO

Author(s)

Christian Bender

References

Laplace prior
Froehlich et. al. 2007, Large scale statistical inference of signaling pathways from RNAi and microarray data.

Scale free prior
Kamimura and Shimodaira, A Scale-free Prior over Graph Structures for Bayesian Inference of Gene Networks

See Also

ddepn

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

ddepn documentation built on May 2, 2019, 4:42 p.m.