Description Usage Arguments Details Value Author(s) See Also Examples

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

1 |

`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. |

* 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.

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.

Christoph Rau

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## 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)
``` |

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