Selects the top N strongest edges of the chosen module(s) based on the consensus network and calculates a divergence score for each edge.
Usage
calculateEdgeDivergence(
module_names,
pruned_modules,
consensus_network,
network_list,
replicate2species,
N = Inf,
n_cores = 1L
)Arguments
- module_names
Character or character vector, the name(s) of the module(s) of interest.
- pruned_modules
Data frame of the pruned modules, required columns:
- regulator
Character, transcriptional regulator.
- target
Character, target gene of the transcriptional regulator (member of the regulator's pruned module).
- consensus_network
igraphobject, the consensus network across all species and replicates.- network_list
A list of
igraphobjects containing the networks per replicate.- replicate2species
A data frame that specifies which species each replicate belongs to, required columns:
- replicate
Character, name of the replicate.
- species
Character, name of the species.
- N
Integer, the number of strongest edges to subset. If set to Inf (default), all edges in the module are considered.
- n_cores
Integer, the number of cores (default: 1).
Value
A data frame of the selected edges with 5 columns:
- regulator
Character, transcriptional regulator.
- from, to
Character, the 2 member genes of the regulator's module that form the edge.
- consensus_weight
Numeric, consensus edge weight/adjacency (the weighted average of replicate-wise adjacencies).
- f_statistic
Numeric, measue of edge divergence. It is calculated as the F-statistic from the ANOVA of edge weights with species as groups.
- p-value
Numeric, the p-value of the F-statistic.
Details
In the CroCoNet approach, networks are reconstructed per replicate and combined into a single phylogeny-aware consensus network which is the basis of the module assignment.
This function selects the intramodular edges of the input module(s) in the consensus network. If N is set to Inf, all intramodular edges are considered for the divergence calculation, if N is smaller than the module size, the edges are ordered by their consensus edge weight and only the top N edges are kept per module.
For each edge that was kept, an edge divergence score is calculated based on its edge weights in the networks of individual replicates. The edge weights are compared across species using an ANOVA. and the F-statistic (i.e. the variation across the species means / variation within the species) and the p-value of this F-statistic are output as the measures of edge divergence.