This function performs network analysis on the provided data and generates a visualization of the network. It uses IsingFit to model the network structure, clusters nodes using Louvain method, and visualizes the resulting network graph.
Usage
network_analysis(
pids,
entity = "reaction",
remove_singlet = TRUE,
remove_negative_edges = TRUE,
file_name = paste0(project_path, "network.tiff"),
width = 1500,
height = 1500,
labs_size = 1,
min_frequency_term = 0.01,
restriction = "none",
temp_reac = Reac,
temp_indi = Indi,
temp_drug = Drug,
save_plot = TRUE
)Arguments
- pids
Numeric vector of unique identifiers on which the network analysis should be performed.
- entity
Character specifying the type of entity to analyze ("reaction", "indication", or "substance").
- remove_singlet
Logical indicating whether to remove singleton nodes (nodes with no edges). Default is TRUE.
- remove_negative_edges
Logical indicating whether to remove edges with negative weights. Default is TRUE.
- file_name
Character string specifying the file name (including path) to save the network visualization. Default is "network.tiff".
- width
Numeric specifying the width of the saved image in pixels. Default is 1500.
- height
Numeric specifying the height of the saved image in pixels. Default is 1500.
- labs_size
Size of labels in network visualization. Default is 1. It can be changed if visualization is not good.
- min_frequency_term
Frequency threshold for a term in the dataset to be included in the analysis. Default to 0.01
- restriction
Restriction performed in the analysis. Default is none. It could be set to 'suspects' if entity is 'substance' to restrict the analysis to primary and secondary suspects
- temp_reac
Reac dataset. Can be set to sample_Reac for testing
- temp_indi
Indi dataset. Can be set to sample_Indi for testing
- temp_drug
Drug dataset. Can be set to sample_Drug for testing
- save_plot
Whether the plot should be saved as a tiff. Defaults to true
References
Fusaroli M, Raschi E, Gatti M, De Ponti F, Poluzzi E. Development of a Network-Based Signal Detection Tool: The COVID-19 Adversome in the FDA Adverse Event Reporting System. Front Pharmacol. 2021 Dec 8;12:740707. doi: 10.3389/fphar.2021.740707. PMID: 34955821; PMCID: PMC8694570.
Fusaroli, M., Polizzi, S., Menestrina, L. et al. Unveiling the Burden of Drug-Induced Impulsivity: A Network Analysis of the FDA Adverse Event Reporting System. Drug Saf (2024). https://doi.org/10.1007/s40264-024-01471-z
Examples
# Perform network analysis for reactions with specified pids
net_plot <- network_analysis(
pids = sample_Demo$primaryid,
entity = "reaction", temp_reac = sample_Reac,
save_plot = FALSE
)
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#> Joining with `by = join_by(s)`
plot(net_plot)
