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Get subnetwork from INDRA database

getSubnetworkFromIndra.Rd

Using differential abundance results from MSstats, this function retrieves a subnetwork of protein interactions from INDRA database.

Usage

getSubnetworkFromIndra(
  input,
  protein_level_data = NULL,
  pvalueCutoff = NULL,
  statement_types = NULL,
  paper_count_cutoff = 1,
  evidence_count_cutoff = 1,
  correlation_cutoff = 0.3,
  sources_filter = NULL,
  logfc_cutoff = NULL,
  force_include_other = NULL,
  filter_by_curation = FALSE,
  filter_by_ptm_site = FALSE,
  include_infinite_fc = FALSE,
  direction = c("both", "up", "down")
)

Arguments

input

output of groupComparison function's comparisionResult table, annotated by annotateProteinInfoFromIndra. Must contain Protein, EntityNamespace, and EntityId columns (and typically also EntityName, log2FC, adj.pvalue). When an analyte grounds to multiple candidates the three Entity* columns are semicolon-joined and positionally aligned.

protein_level_data

Deprecated, and will be removed in a future release. Output of the dataProcess function's ProteinLevelData table, used to annotate edges with correlations and apply correlation_cutoff. Supplying it gives a deprecation warning.

pvalueCutoff

p-value cutoff for filtering. Default is NULL, i.e. no filtering

statement_types

list of interaction types to filter on. Equivalent to statement type in INDRA. Default is NULL.

paper_count_cutoff

Deprecated, and will be removed in a future release. It is ignored: paper counts are not available from INDRA, so this filter never had an effect for 1 and removed every edge for larger values. Supplying it gives a deprecation warning.

evidence_count_cutoff

number of evidence to filter on for each paper. E.g. A paper may have 5 sentences describing the same interaction vs 1 sentence. Default is 1.

correlation_cutoff

Deprecated, and will be removed in a future release. If protein_level_data is not NULL, remove edges whose absolute correlation is below this cutoff. Default is 0.3. Supplying it gives a deprecation warning.

sources_filter

filtering only on specific sources. Default is no filter, i.e. NULL. See INDRA_DATABASE_SOURCES for INDRA's source names. Otherwise, should be a list, e.g. c('reach', 'medscan').

logfc_cutoff

absolute log fold change cutoff for filtering proteins. Only proteins with |logFC| greater than this value will be retained. Default is NULL, i.e. no logFC filtering.

force_include_other

character vector of identifiers to include in the network, regardless if those ids are in the input data. Should be formatted as "namespace:identifier", e.g. "HGNC:1234" or "CHEBI:4911".

filter_by_curation

logical, whether to filter out statements that have been curated as incorrect in INDRA. Default is FALSE. Runs filter_by_curation() with min_evidence = evidence_count_cutoff.

filter_by_ptm_site

logical, whether to filter edges based on whether the site information from INDRA matches with the PTM site in the input. Default is FALSE. Only applicable for differential PTM abundance results.

include_infinite_fc

logical, whether to include proteins with infinite log fold change (i.e. proteins that are only detected in one condition). Default is FALSE.

direction

Character string specifying the direction of regulation to include. One of "both" (default), "up" (upregulated only), or "down" (downregulated only).

Value

list of 2 data.frames, nodes and edges, that meets the contract checked by validate_network.

edges has one row per INDRA statement and pair of nodes it connects (a statement whose grounding matches several Protein rows, such as two isoforms of one gene, gives each of them an edge): source, target, interaction (INDRA statement type), directed (FALSE for symmetric types such as Complex), site (PTM site on the target, or NA), confidence (INDRA belief score), evidence_count, evidence_url (INDRA page for this statement), statement_id (INDRA statement hash, as character), backend_database ("INDRA"), query_type ("subnetwork"), evidence_sources (evidence count per source, as JSON), and the deprecated paperCount (and correlation when protein_level_data is given).

nodes has one row per analyte (one per site for PTM input, all with the protein's id): id, entity_type ("protein", "ptm_site", "metabolite", ...), entity_name, namespace, entity_id, measured (TRUE for analytes in input), included_in_query, node_role ("passed_cutoffs", or "user_added" for nodes that are there only through force_include_other), site, has_measured_sites (TRUE for proteins with PTM site rows), logFC, and adj.pvalue. Nodes from force_include_other that are not in input have measured = FALSE and NA statistics.

Examples

input <- data.table::fread(system.file(
    "extdata/groupComparisonModel.csv",
    package = "MSstatsBioNet"
))
subnetwork <- getSubnetworkFromIndra(input)
#> INDRA subnetwork: how are 10 selected proteins connected to each other, with no other nodes added?
#> Warning: NOTICE: This function includes third-party software components
#>         that are licensed under the BSD 2-Clause License. Please ensure to
#>         include the third-party licensing agreements if redistributing this
#>         package or utilizing the results based on this package.
#>         See the LICENSE file for more details.
head(subnetwork$nodes)
#>       id entity_type entity_name namespace entity_id measured included_in_query
#> 1 O00217     protein      NDUFS8      HGNC      7715     TRUE              TRUE
#> 2 O60313     protein        OPA1      HGNC      8140     TRUE              TRUE
#> 3 O75306     protein      NDUFS2      HGNC      7708     TRUE              TRUE
#> 4 P05023     protein      ATP1A1      HGNC       799     TRUE              TRUE
#> 5 P05067     protein         APP      HGNC       620     TRUE              TRUE
#> 6 P05090     protein        APOD      HGNC       612     TRUE              TRUE
#>        node_role site has_measured_sites     logFC  adj.pvalue
#> 1 passed_cutoffs <NA>              FALSE 2.0285031 0.013821932
#> 2 passed_cutoffs <NA>              FALSE 0.9299641 0.019584180
#> 3 passed_cutoffs <NA>              FALSE 2.4745040 0.004457034
#> 4 passed_cutoffs <NA>              FALSE 1.8391155 0.003251073
#> 5 passed_cutoffs <NA>              FALSE 0.7360012 0.020306662
#> 6 passed_cutoffs <NA>              FALSE 0.5683951 0.013715050
head(subnetwork$edges)
#>   source target    interaction directed site confidence evidence_count
#> 1 O75306 O00217        Complex    FALSE <NA>  0.7981153              3
#> 2 P08574 O75306        Complex    FALSE <NA>  0.8302067              1
#> 3 P05067 O60313 DecreaseAmount     TRUE <NA>  0.4850742              3
#> 4 P05067 P05362 IncreaseAmount     TRUE <NA>  0.5028536              3
#> 5 P05067 P05090 IncreaseAmount     TRUE <NA>  0.4715316              2
#> 6 P08574 P05023        Complex    FALSE <NA>  0.8302067              1
#>                                                               evidence_url
#> 1  https://db.indra.bio/statements/from_hash/35453498871697150?format=html
#> 2   https://db.indra.bio/statements/from_hash/6349003830434161?format=html
#> 3 https://db.indra.bio/statements/from_hash/-14390698362022978?format=html
#> 4  https://db.indra.bio/statements/from_hash/29503060009374668?format=html
#> 5  https://db.indra.bio/statements/from_hash/13542679335950199?format=html
#> 6 https://db.indra.bio/statements/from_hash/-27854856016676857?format=html
#>         statement_id backend_database query_type evidence_sources paperCount
#> 1  35453498871697150            INDRA subnetwork   {"biogrid": 3}          1
#> 2   6349003830434161            INDRA subnetwork   {"biogrid": 1}          1
#> 3 -14390698362022978            INDRA subnetwork     {"reach": 3}          1
#> 4  29503060009374668            INDRA subnetwork     {"reach": 3}          1
#> 5  13542679335950199            INDRA subnetwork     {"reach": 2}          1
#> 6 -27854856016676857            INDRA subnetwork   {"biogrid": 1}          1