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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
)

Arguments

input

output of groupComparison function's comparisionResult table, which contains a list of proteins and their corresponding p-values, logFCs, along with additional HGNC ID and HGNC name columns

protein_level_data

output of the dataProcess function's ProteinLevelData table, which contains a list of proteins and their corresponding abundances. Used for annotating correlation information and applying correlation cutoffs.

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

number of papers to filter on. Default is 1.

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

if protein_level_abundance is not NULL, apply a cutoff for edges with correlation less than a specified cutoff. Default is 0.3

sources_filter

filtering only on specific sources. Default is no filter, i.e. NULL. 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.

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.

Value

list of 2 data.frames, nodes and edges

Examples

input <- data.table::fread(system.file(
    "extdata/groupComparisonModel.csv",
    package = "MSstatsBioNet"
))
subnetwork <- getSubnetworkFromIndra(input)
#> 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$nodes)
#>        id hgncName   Site     logFC  adj.pvalue
#>    <char>   <char> <char>     <num>       <num>
#> 1: O00217   NDUFS8   <NA> 2.0285031 0.013821932
#> 2: O60313     OPA1   <NA> 0.9299641 0.019584180
#> 3: O75306   NDUFS2   <NA> 2.4745040 0.004457034
#> 4: P05023   ATP1A1   <NA> 1.8391155 0.003251073
#> 5: P05067      APP   <NA> 0.7360012 0.020306662
#> 6: P05090     APOD   <NA> 0.5683951 0.013715050
head(subnetwork$edges)
#>   source target site interaction evidenceCount paperCount
#> 1 P05023 O75306 <NA>     Complex             1          1
#> 2 O75306 P08574 <NA>     Complex             1          1
#> 3 P05067 O60313 <NA>  Activation             2          1
#> 4 O60313 O00217 <NA>     Complex             1          1
#> 5 P05362 P05067 <NA>     Complex            13          1
#> 6 O75306 P05067 <NA>     Complex             1          1
#>                                                                                 evidenceLink
#> 1  https://db.indra.bio/statements/from_agents?subject=799@HGNC&object=7708@HGNC&format=html
#> 2 https://db.indra.bio/statements/from_agents?subject=7708@HGNC&object=2579@HGNC&format=html
#> 3  https://db.indra.bio/statements/from_agents?subject=620@HGNC&object=8140@HGNC&format=html
#> 4 https://db.indra.bio/statements/from_agents?subject=8140@HGNC&object=7715@HGNC&format=html
#> 5  https://db.indra.bio/statements/from_agents?subject=5344@HGNC&object=620@HGNC&format=html
#> 6  https://db.indra.bio/statements/from_agents?subject=7708@HGNC&object=620@HGNC&format=html
#>                  sourceCounts          stmt_hash
#> 1              {"biogrid": 1}  -5813063534036006
#> 2              {"biogrid": 1}   6349003830434161
#> 3                {"reach": 2}   3948742039105656
#> 4              {"biogrid": 1} -19747883270157675
#> 5 {"sparser": 10, "reach": 3} -20220236678417803
#> 6              {"biogrid": 1}  22463147519060585