ICMg.links.sampler: ICMg.links.sampler

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

N x 2 matrix of link endpoints (N = number of links).

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.

Value

Returns samples as a list:
zS.num x N matrix of samples of component assignments for links.
convVector 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.

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.

References

Parkkinen, J. and Kaski, S. Searching for functional gene modules with interaction component models. BMC Systems Biology 4 (2010), 4.

Examples

Run this code

data(osmo) # Load data

## Run ICMg links sampler	
res <- ICMg.links.sampler(osmo$ppi, C=10)

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