diamond: DIAMOnD
In ddeweerd/MODifieRDev: MODifieRDev

Description Usage Arguments Details Value Author(s) References

A seed gene based algorithm to identify disease module from differentially expressed genes

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diamond(MODifieR_input, ppi_network, deg_cutoff = 0.05,
  n_output_genes = 200, seed_weight = 10, include_seed = FALSE,
  dataset_name = NULL)

`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
`deg_cutoff`	p-value cutoff for differentialy expressed genes
`n_output_genes`	maximum number of genes to be included in the final module
`seed_weight`	Numeric additional parameter to assign weight for the seed genes
`include_seed`	Logical TRUE/FALSE for inclusion of seed genes in the output module
`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

A slightly modified version of the original DIAMOnD python script is called from within R. The only change to the orginal algorithm is the option to include the seed genes to the module. There are also function to add or remove the seed genes from the output object, namely: diamond_add_seed_genes and diamond_remove_seed_genes For a detailed description of how the algorithm works, please see the paper referenced below.

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

`module_genes`	A character vector containing the genes in the final module
`seed_genes`	Character vector containing genes that have been used as seed genes in the algorithm
`ignored_genes`	Potential seed genes that are not in the PPi network
`added_genes`	A table containing information on all added genes. First column is the name of the gene, the second column is the degree of the node (gene). The third column is the number of +1 neighbors and the fourth column is the p-value.
`settings`	A named list containing the parameters used in generating the object

Dirk de Weerd

Ghiassian, S. D., Menche, J., & Barabási, A. L. (2015). A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome. PLoS Computational Biology, 11(4), 1–21. https://doi.org/10.1371/journal.pcbi.1004120

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