Calculates one or both of two preservation statistics - correlation of adjacencies (cor_adj) and correlation of intramodular connectivities (cor_kIM) - for all modules and replicate pairs with or without jackknifing.
Usage
calculatePresStats(
pruned_modules,
network_list,
stats = c("cor_adj", "cor_kIM"),
replicate2species = NULL,
jackknife = TRUE,
signed = FALSE,
n_cores = 1L,
corr_method = "spearman"
)Arguments
- pruned_modules
Data frame of pruned modules, required columns:
- regulator
Character, transcriptional regulator.
- target
Character, target gene of the transcriptional regulator (member of the regulator's pruned module).
- network_list
A named list of
igraphobjects containing the networks of all replicates.- stats
Character or character vector specifying which preservation statistics to calculate (one or more of "cor_adj", "cor_kIM", default: c("cor_adj", "cor_kIM")).
- replicate2species
A data frame specifying which species each replicate belongs to, required columns:
- replicate
Character, name of the replicate.
- species
Character, name of the species.
If NULL (default), the output will contain no species information.
- jackknife
Logical specifying whether jackknifing should be performed or not (default: TRUE).
- signed
Logical indicating whether the networks in
network_listare signed (default: FALSE, see alsonormalizeEdgeWeights). If set to FALSE andnetwork_listcontains the edge attribute "direction", the edge weights in the network with direction "-" are negated for the calculation of "cor_adj".- n_cores
Integer, the number of cores (default: 1).
- corr_method
Character, the method for the calculation of correlation, one of "spearman", "pearson", "kendall" (default: "spearman").
Value
Data frame of the preservation statistics with the following columns:
- regulator
Character, transcriptional regulator.
- module_size
Integer, the number of target genes assigned to a regulator (only present if the column is also present in
pruned_modules).- type
Character, module type ("orig" = original or "jk" = jackknifed, only present if parameter
jackknifeis set to TRUE).- id
Character, the unique ID of the module version (format: nameOfRegulator_jk_nameOfGeneRemoved in case of module type "jk" and nameOfRegulator_orig in case of module type "orig", only present if parameter
jackknifeis set to TRUE).- gene_removed
Character, the name of the gene removed by jackknifing (NA in case of module type "orig", only present if parameter
jackknifeis set to TRUE).- replicate1, replicate2
Character, the names of the replicates compared.
- species1, species2
Character, the names of the species
replicate1andreplicate2belong to, respectively (only present ifreplicate2speciesis not NULL).- {{nameOfStat}}
Numeric, one or more columns containing the preservation statistics specified by the parameter
stats.
Details
The function calculates two preservation statistics adapted from WGCNA (cor_adj and cor_kIM) for each module and each replicate pair. Both statistics quantify how well the topology of a module is preserved between the networks of two replicates. The statistic cor_adj is the correlation of all edge weights within the module in the network of replicate1 VS in the network of replicate2, while the statistic cor_kIM is the correlation of the intramodular connectivities per module member gene in the network of replicate1 VS in the network of replicate2.
All statistics assume a joint module assignment (typically derived from the consensus network) but compare topological properties directly between the replicate-wise networks. In this approach, a module is always defined as the same set of genes, but the adjacencies/edge weights among these genes could differ from replicate to replicate; poorly preserved modules are expected to have many, while well-preserved modules are expected to have few such differences.
If jackknife is set to TRUE (the default), the function creates all possible jackknifed versions of each input module by removing each target gene assigned to that module (the regulator is never excluded), then it calculates the preservation statistics for all of these jackknifed module versions in addition to the original module. This way, a confidence interval can be calculated for each module and statistic (see summarizeJackknifeStats). Later on in the pipeline, jackknifing can also provide information about which target genes within a conserved/diverged module are particularly responsible for the conservation/divergence (see findConservedDivergedTargets). If jackknifing is not desired, please set jackknife to FALSE. This will also substantially reduce running times.