Combine networks from several queries or backends

Combines the networks from several get_network() calls, for example two queries against one backend, or one query against two backends, into one network that meets the same contract (validate_network()).

Usage

merge_networks(..., entities = NULL)

Arguments

...

networks: lists of nodes and edges from get_network()

entities

NULL (default), or the entity table from prepare_entities() to recompute node status from

Value

list of nodes and edges, plus regulators and provenance when any network has them

Details

Edges are the same edge when they have the same source, target, backend_database, and statement_id. One row is kept per edge: the copy with the largest evidence_count (the first one on ties), with the query_type values of all copies joined by ";", e.g. "subnetwork;mediated". A message names the statements whose copies differ in evidence_count or confidence. The same relation from two backends stays two rows, one per backend_database, since each has its own confidence and evidence_url; confidences are never combined across backends. Columns that only some networks have are filled with NA.

Nodes are the same node when they have the same id and site (a protein has one row per PTM site). Their node_role values are joined by ";", e.g. "passed_cutoffs;mediator", included_in_query is TRUE if it is in any network, and other columns take the first non-NA value. has_measured_sites describes the protein, so it is TRUE on every row of a protein that has it in any network: merging a protein network with a PTM network marks the protein's protein-level row too. included_in_query describes the query, so it can differ between networks built from one entity table: a node can be a latent regulator in one query and part of another.

Without entities, measured, logFC, and adj.pvalue must agree for each node, because networks built from one entity table always agree; otherwise merge_networks() stops and names the nodes. To merge networks built from different entity tables, pass the entity table as entities: measured, logFC, adj.pvalue, and has_measured_sites are then recomputed from it (nodes not in it are measured = FALSE with NA statistics).

The regulators and provenance tables of the networks, when present, are combined by row, with columns filled with NA. Other list elements are dropped, with a message.

See also

get_network(), validate_network()

Examples

# \donttest{
input <- data.table::fread(system.file(
    "extdata/groupComparisonModel.csv",
    package = "MSstatsBioNet"
))
indra <- indra_backend()
entities <- prepare_entities(input, entity_type = "protein",
                             id_type = "uniprot")
entities <- convert_ids(indra, entities)
# Two selections from one entity table: the edges among the proteins
# that pass both cutoffs are in both networks
loose <- get_network(indra, select_entities(entities, pvalue_cutoff = 0.05))
#> INDRA subnetwork: how are 10 selected proteins connected to each other, with no other nodes added?
strict <- get_network(indra, select_entities(entities, pvalue_cutoff = 0.01))
#> INDRA subnetwork: how are 5 selected proteins connected to each other, with no other nodes added?
network <- merge_networks(loose, strict)
# The shared edges come back once
c(loose = nrow(loose$edges), strict = nrow(strict$edges),
  merged = nrow(network$edges))
#>  loose strict merged 
#>     31      2     31 
# }