Description Usage Arguments Details Value Examples

This function receives an MxN matrix with named columns (will be used to name vertices), and returns a list of igrpah graphs representing the resulting clusters (e.g. symptom clusters of "patient communities").

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`mat` |
MxN matrix |

`nrows` |
number of rows to analyze (defaults to all rows) |

`ncols` |
number of columns to analyze (defaults to all columns) |

`thresh` |
integer threshld below which entries (e.g. symptom scores) will be set to 0 (i.e. considered as non-present) |

`detectAlgo` |
string value, the type of network community detection algorithm to use (defaults to 'WT') |

`simil_measure` |
string value, similarity function used (string value), can be either 'ARI' (default) or 'Euclidean'. |

`centrality` |
string value, centrality measure to use for indicating central nodes (via node size in the igraph networks) |

`cluster_colors` |
string value, |

`sparsify` |
number in [0,100] percentage of the weakest network edges to remove from the centrality computation, to allow central nodes to be detected more easily. |

`reactive_dom` |
reactive domain in case used in a shiny app (will be used by incProgress()). |

`inProg_amount` |
real number in [0,1], the amount of status bar to fill up (default is 1), in case used in a shiny app (will be used by incProgress()). |

It does so using the method of concordance networks clustering as described in Henry et al. "Concordance networks and application to clustering cancer symptomology - PLOS." 14 Mar. 2018.

The detection algorithm (from the igraph library) can be chosen out of the following list:

'WT' (default) for cluster_walktrap. Community strucure via short random walks. 'FG' for cluster_fast_greedy(), Community structure via greedy optimization of modularity. 'IM' for cluster_infomap(), Infomap community finding. 'LP' for cluster_label_prop(), Finding communities based on propagating labels. 'LE' for cluster_leading_eigen(), Community structure detecting based on the leading eigenvector of the community matrix. 'LV' for cluster_louvain(), Finding community structure by multi-level optimization of modularity.

list consisting of (1) a list of the normalized community networks, and (2) the associated communities (e.g. as generated by igraph::groups(cluster_walktrap(g)))

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