mod_mcode: MCODE

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

View source: R/mcode.R

Description

A clique based algorithm to identify disease modules from differentially expressed genes originally by Bader et al

Usage

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mod_mcode(MODifieR_input, ppi_network, hierarchy = 1, vwp = 0.5,
  haircut = F, fluff = F, fdt = 0.8, loops = T,
  deg_cutoff = 0.05, module_cutoff = 3.5, dataset_name = NULL)

Arguments

MODifieR_input

A MODifieR input object produced by one of the create_input functions

ppi_network

A network as a dataframe where the first 2 columns are the interactions

hierarchy

This parameter indicates how many hierarchy are included in the network, currently it can be 0, 1 or 2. Default value is 1.

vwp

Vertex weight percentage. Default value is 0.5.

haircut

Boolean value, whether to remove singly-connected nodes from clusters (TRUE) or not (FALSE).

fluff

Boolean value, whether to spand cluster cores by one neighbour shell outwards (TRUE) or not (FALSE).

fdt

Cluster density cutoff. Default value is 0.8.

loops

Boolean value, whether to include self-loops (TRUE) or not (FALSE).

deg_cutoff

p-value cutoff for differentialy expressed genes

module_cutoff

Minimal score for a module to be returned

dataset_name

Optional name for the input object that will be stored in the settings object. Default is the variable name of the input object

Details

Much of the code an documentation has been taken from the now defunct package "ProNet"

Value

mcode returns a list of objects of class "MODifieR_module" with subclass "Mcode". The objects are named lists containing the following components:

module_genes

A character vector containing the genes in the final module

module_scores

A numeric value that denotes the score of the module. Higher is better

settings

A named list containing the parameters used in generating the object

Author(s)

DIrk de Weerd

References

Bader, G. D., & Hogue, C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4(1), 2. https://doi.org/10.1186/1471-2105-4-2

See Also

https://github.com/cran/ProNet


ddeweerd/MODifieRDev documentation built on Nov. 12, 2019, 7:50 a.m.