detect_modules: Modules detection in a network

In Kumquatum/GWENA: Pipeline for augmented co-expression analysis

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)

Kumquatum/GWENA documentation built on July 7, 2023, 3:41 p.m.