View source: R/analyse_functional_network.R
| analyse_functional_network | R Documentation | 
The STRING database provides a resource for known and predicted protein-protein interactions.
The type of interactions include direct (physical) and indirect (functional) interactions.
Through the R package STRINGdb this resource if provided to R users. This function
provides a convenient wrapper for STRINGdb functions that allow an easy use within the
protti pipeline.
analyse_functional_network(
  data,
  protein_id,
  string_id,
  organism_id,
  version = "12.0",
  score_threshold = 900,
  binds_treatment = NULL,
  halo_color = NULL,
  plot = TRUE
)
| data | a data frame that contains significantly changing proteins (STRINGdb is only able to plot 400 proteins at a time so do not provide more for network plots). Information about treatment binding can be provided and will be displayed as colorful halos around the proteins in the network. | 
| protein_id | a character column in the  | 
| string_id | a character column in the  | 
| organism_id | a numeric value specifying an organism ID (NCBI taxon-ID). This can be obtained from here. H. sapiens: 9606, S. cerevisiae: 4932, E. coli: 511145. | 
| version | a character value that specifies the version of STRINGdb to be used. Default is 12.0. | 
| score_threshold | a numeric value specifying the interaction score that based on STRING has to be between 0 and 1000. A score closer to 1000 is related to a higher confidence for the interaction. The default value is 900. | 
| binds_treatment | a logical column in the  | 
| halo_color | optional, character value with a color hex-code. This is the color of the halo of proteins that bind the treatment. | 
| plot | a logical that indicates whether the result should be plotted or returned as a table. | 
A network plot displaying interactions of the provided proteins. If
binds_treatment was provided halos around the proteins show which proteins interact with
the treatment. If plot = FALSE a data frame with interaction information is returned.
# Create example data
data <- data.frame(
  uniprot_id = c(
    "P0A7R1",
    "P02359",
    "P60624",
    "P0A7M2",
    "P0A7X3",
    "P0AGD3"
  ),
  xref_string = c(
    "511145.b4203;",
    "511145.b3341;",
    "511145.b3309;",
    "511145.b3637;",
    "511145.b3230;",
    "511145.b1656;"
  ),
  is_known = c(
    TRUE,
    TRUE,
    TRUE,
    TRUE,
    TRUE,
    FALSE
  )
)
# Perform network analysis
network <- analyse_functional_network(
  data,
  protein_id = uniprot_id,
  string_id = xref_string,
  organism_id = 511145,
  binds_treatment = is_known,
  plot = TRUE
)
network
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