detect_modules {GWENA} | R Documentation

## Modules detection in a network

### Description

Detect the modules by hierarchical clustering .

### Usage

    detect_modules(
      data_expr,
      network,
      min_module_size = min(20, ncol(data_expr)/2),
      clustering_th = NULL,
      merge_close_modules = TRUE,
      merge_threshold = 0.75,
      detailled_result = TRUE,
      pam_respects_dendro = FALSE,
      ...
    )

### Arguments

`data_expr` | matrix or data.frame or SummarizedExperiment, expression data with genes as column and samples as row.
`network` | matrix or data.frame, strengh of gene co-expression (edge values).
`min_module_size` | integer, lowest number of gene allowed in a module. If none provided, estimated.
`clustering_th` | float, threshold to be used by the clustering method. For now `cutreeDynamic`.
`merge_close_modules` | boolean, does closest modules (based on eigengene) should be merged together.
`merge_threshold` | float, eigengenes correlation value over which close modules will be merged. Must be in ]0;1[. See `mergeCloseModules`
`detailled_result` | boolean, does pre-merge modules (if applicable) and dendrogram included in output.
`pam_respects_dendro` | boolean, If TRUE, the Partitioning Around Medoids (PAM) stage will respect the dendrogram in the sense that objects and small clusters will only be assigned to clusters that belong to the same branch that the objects or small clusters being assigned belong to.
`...` | any other parameter compatible with `mergeCloseModules`

### Value

list containing modules detected, modules_eigengenes, and if asked for, modules pre-merge and dendrograms of genes and merged modules

### Examples

    df <- kuehne_expr[1:24, 1:350]
    net <- build_net(df, n_threads = 1)
    detect_modules(df, net$network)

[Package GWENA version 1.2.0 Index]