ICMg.links.sampler.SeedsAndWeights: wMICA: weighted Maximal Information Component Analysis

In ChristophRau/wMICA: weighted Maximal Information Component Analysis

wMICA: weighted Maximal Information Component Analysis

Description

ICMg.links.sampler.SeedsAndWeights calculates the module memberships for the wMICA package and is a modified weighted version of the ICMg algorithm.

Usage

ICMg.links.sampler.SeedsAndWeights(L, C, Seeds=c(), Weight=0, alpha=10, beta=0.01,  B.num=8, B.size=100, S.num=20, S.size=10, C.boost=1)

Arguments

L	N x 3 matrix of link endpoints (N = number of links). Third column is the edge strength. A Nx2 matrix will also be accepted, with assumed weights of 1
C	Number of components.
Seeds	a Kx2 matrix (where K is the number of genes to be seeded into a module) with the first column containing the gene number (as in L) and the second column being the desired module to be seeded into
Weight	the number of iterations to weight each seeded gene by
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)

Christoph Rau

See Also

ICMg.links.sampler.Seeds

Examples

	## Load data and set parameters
	data(osmo)
	C.boost = 1 ## Use faster C funtions
	Seeds=c()
  Weight=0
  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$ppi, C, Seeds, Weight, alpha, beta, B.num, B.size, S.num, S.size, C.boost) 
	## Compute component membership probabilities for nodes
  	res$comp.memb <- ICMg.get.comp.memberships(osmo$ppi, res)
  	## Compute (hard) clustering for nodes
  	res$clustering <- apply(res$comp.memb, 2, which.max)

ChristophRau/wMICA documentation built on May 3, 2024, 3:21 a.m.