getCluster_methods {BioTIP} | R Documentation

## Clustering Network Nodes

### Description

This function runs over all states which are grouped samples. For each state, this function splits the correlation network generated from the function `getNetwork` into several sub-networks (which we called 'module'). The network nodes will be defined by the end-user. For transcriptome analysis, network nodes can be the expressed transcripts. The outputs of this function include the module IDs and node IDs per module.

### Usage

    getCluster_methods(
      igraphL,
      method = c("rw", "hcm", "km", "pam", "natural"),
      cutoff = NULL
    )

### Arguments

`igraphL` | A list of numerical matrices or a list of igraph objects. The list of igraph objects can be the output from the getNetwork function.
`method` | A mathematical clustering model for analyzing network nodes. Default is a random walk ('rw'). A method could be 'rw', 'hcm', 'km', 'pam', or 'natural', where: rw: random walk using cluster_walktrap function in igraph package. 'igraphL' has to be a list of igraph. hcm: hierarchical clustering using function hclust) and dist, using method 'complete'. km and pam: k-medoids or PAM algorithm using KMedoids. natrual: if nodes are disconnected, they may naturally cluster and form sub-networks.
`cutoff` | A numeric value, default is NULL. For each method it means: rw: the number of steps needed, see cluster_walktrap for more detail. If "cutoff" is not assigned, default of 4 will be used. hcm, km and pam: number of clusters wanted. No default assigned. natural: does not use this parameter.

### Value

When method=rw: A list of `communities` objects of R package igraph, whose length is the length of the input object `igraphL`. These `communities` objects can be used for visualization when being assigned to the 'mark.groups' parameter of the `plot.igraph` function of the igraph package. Otherwise this function returns a list of vectors, whose length is the length of the input object `igraphL`. The names of each vector are the pre-selected transcript IDs by th function `sd_selection`. Each vector, whose length is the number of pre-selected transcript in a state, contains the module IDs.

### Author(s)

Zhezhen Wang zhezhen@uchicago.edu

### Examples

    test = list('state1' = matrix(sample(1:10, 6), 3, 3), 'state2' =
    matrix(sample(1:10, 6), 3, 3), 'state3' = matrix(sample(1:10, 6), 3, 3))
    #assign colnames and rownames to the matrix

    for(i in names(test)){
    colnames(test[[i]]) = 1:3
    row.names(test[[i]]) = 1:3}

    #using 'rw' or 'natural' method
    igraphL <- getNetwork(test,  fdr=1)
    #[1] "state1:3 nodes"
    #[1] "state2:3 nodes"
    #[1] "state3:3 nodes"

    cl <- getCluster_methods(igraphL)

    #using 'km',  'pam' or 'hcm'
    cl <- getCluster_methods(test,  method = 'pam',  cutoff=2)

[Package BioTIP version 1.6.0 Index]