ICMg.links.sampler {ICMg}R Documentation

ICMg: inferring gene modules from interaction data.

Description

ICMg.links.sampler computes samples from the posterior of the assignments of datapoints (interactions) to latent components. From these we can then obtain component membership distributions and clusterings for genes.

Usage

ICMg.links.sampler(L, C, alpha=10, beta=0.01,  B.num=8, B.size=100, S.num=20, S.size=10, C.boost=1)

Arguments

L N x 2 matrix of link endpoints (N = number of links).
C Number of components.
alpha Hyperparameter describing the global distribution over components, larger alpha gives a more uniform distribution.
beta Hyperparameter describing the component-wise distributions over nodes, larger beta gives a more uniform distribution.
B.num Number of burnin rounds.*
B.size Size of one burnin round.*
S.num Number of sample rounds.*
S.size Size of one sample round.*
C.boost Set to 1 to use faster iteration with C, set to 0 to use slower R functions.

Details

* One run consists of two parts, during burnin the sampler is expected to mix, after which the samples are taken. Information about convergence (convN and convL are estimates of convergence for link and node sampling, respectively) and component sizes are printed after each burnin/sample round. For example: B.num=8, B.size=100, S.num=20, S.size=10, runs 800 burnin iterations in 8 rounds and then takes 20 samples with an interval of 10 iterations.

Value

Returns samples as a list:

z S.num x N matrix of samples of component assignments for links.
conv Vector of length (B.num + S.num) with convergence estimator values for link sampling.
counts (B.num + S.num) x C matrix of link component sizes.

additionally all parameters of the run are included in the list.

Author(s)

Juuso Parkkinen

See Also

ICMg.combined.sampler

Examples

        ## Load data and set parameters
        data(osmo)
        C.boost = 1 ## Use faster C funtions
        alpha = 10
        beta = 0.01
        B.num = 10
        B.size = 10
        S.num = 10  
        S.size = 10
        C = 24

        ## Run sampling
        res = ICMg.links.sampler(osmo$ppi, C, alpha, beta, B.num, B.size, S.num, S.size, C.boost) 
        ## Compute component membership probabilities for nodes
        res$comp.memb <- ICMg.get.comp.memberships(osmo$ppi, res)
        ## Compute (hard) clustering for nodes
        res$clustering <- apply(res$comp.memb, 2, which.max)
[Package ICMg version 1.0 Index]