Description Usage Arguments Details Value Author(s) References
View source: R/clique_sum_permutation.R
An implementation of the clique-based disease module inference method proposed by Gustafsson et al.
1 2 3 | clique_sum_permutation(MODifieR_input, db, n_iterations = 10000,
clique_significance = 0.01, min_clique_size = 5,
multiple_cores = T, n_cores = 4, dataset_name = NULL)
|
MODifieR_input |
A MODifieR input object produced by one of the |
db |
A clique database created by |
n_iterations |
Number of iterations to be performed for the permutation based p-value |
clique_significance |
p-value for cliques to be considered significant |
min_clique_size |
Minimal size for cliques |
multiple_cores |
Parallel process using multiple cores? |
n_cores |
Number of cores to use |
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 |
Clique_sum_permutation finds cliques of at least size min_clique_size
that are
significantly enriched with DEGs. For every clique size, a null distribution is created using
the summed -log 10 p-values. The union of maximal cliques with a summed -log 10 p-value below clique_significance
and at
least min_deg_in_clique
is the final disease module.
clique_sum_permutation returns an object of class "MODifieR_module" with subclass "Clique_Sum_permutation". This object is a named list containing the following components:
module_genes |
A character vector containing the genes in the final module |
settings |
A named list containing the parameters used in generating the object |
Dirk de Weerd
Gustafsson, M., Edström, M., Gawel, D., Nestor, C. E., Wang, H., Zhang, H., … Benson, M. (2014). Integrated genomic and prospective clinical studies show the importance of modular pleiotropy for disease susceptibility, diagnosis and treatment. Genome Medicine, 6(2), 17. https://doi.org/10.1186/gm534
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