moda: MODA

In ddeweerd/MODifieRDev: MODifieRDev

Description

An implementation of MODA co-expression based algorithm.

Usage

moda(MODifieR_input, cutmethod = "Density", group_of_interest,
  specificTheta = 0.1, conservedTheta = 0.1, dataset_name = NULL)

Arguments

MODifieR_input	A MODifieR input object produced by one of the create_input functions
cutmethod	cutting the dendrogram based on maximal average Density or Modularity
group_of_interest	Numerical value denoting which group contains the condition of interest (1 or 2)
specificTheta	the threshold to define min(s)+specificTheta, less than which is considered as condition specific module. s is the sums of rows in Jaccard index matrix. See supplementary file.
conservedTheta	The threshold to define max(s)-conservedTheta, greater than which is considered as condition conserved module. s is the sums of rows in Jaccard index matrix. See supplementary file.
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

This implementation follows a workflow as described in the MODA vignette. First, two separate networks are constructed, a background network containing expression data from all samples and a condition specific network consisting of all samples minus the condition specific samples. Then, hierarchical clustering is performed and cutting height estimated from either maximal average density or modularity

Condition specific co-expression modules are then extracted using the Jaccard index and specificTheta.

The final module will consist of the co-expression module that has the minimal Jaccard index complemented by co-expression modules that have a Jaccard index below this minimal + specificTheta

After analysis, the specificTheta and thereby the disease module can be adjusted using moda_change_specific_threshold

Value

moda returns an object of class "MODifieR_module" with subclass "MODA". This object is a named list containing the following components:

module_genes	A character vector containing the genes in the final module
group1_modules	A list containing all co-expression modules in the background network
group2_modules	A list containing all co-expression modules in the condition specific network
jaccard_table	A matrix with all Jaccard indexes for all co-expression modules
settings	A named list containing the parameters used in generating the object

Author(s)

Dirk de Weerd

References

Li D, Brown JB, Orsini L, Pan Z, Hu G, He S (2016). MODA: MODA: MOdule Differential Analysis for weighted gene co-expression network. R package version 1.6.0

See Also

https://bioconductor.org/packages/release/bioc/vignettes/MODA/inst/doc/MODA.html

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