Type:	Package
Title:	Tool for Unbiased Literature Searching and Gene List Curation
Version:	1.0.1
Description:	Designed for genomic and proteomic data analysis, enabling unbiased PubMed searching, protein interaction network visualization, and comprehensive data summarization. This package aims to help users identify novel targets within their data sets based on protein network interactions and publication precedence of target's association with research context based on literature precedence. Methods in this package are described in detail in: Douglas (Year) <to-be-added DOI or link to the preprint>. Key functionalities of this package also leverage methodologies from previous works, such as: - Szklarczyk et al. (2023) <doi:10.1093/nar/gkac1000> - Winter (2017) <doi:10.32614/RJ-2017-066>.
License:	MIT + file LICENSE
Encoding:	UTF-8
Imports:	rentrez, ComplexHeatmap, circlize, STRINGdb, data.table, igraph, ggplot2, openxlsx, dplyr, tidyr, magrittr, tibble, ggrepel
RoxygenNote:	7.3.2
URL:	https://github.com/camdouglas/DeSciDe
BugReports:	https://github.com/camdouglas/DeSciDe/issues
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, withr
VignetteBuilder:	knitr
Config/testthat/edition:	3
Depends:	R (≥ 4.0.0)
NeedsCompilation:	no
Packaged:	2025-06-20 16:51:49 UTC; seathlab
Author:	Cameron Douglas [aut, cre]
Maintainer:	Cameron Douglas <camerondouglas@ufl.edu>
Repository:	CRAN
Date/Publication:	2025-06-20 18:30:02 UTC

Combine PubMed and STRING Metrics

Description

Combine PubMed search summary and STRING gene metrics.

Usage

combine_summary(
  pubmed_search_results,
  string_results,
  file_directory = NULL,
  export_format = "csv",
  export = FALSE,
  threshold_percentage = 20
)

Arguments

Value

A data frame with combined summary including connectivity, precedence, and category.

Examples

pubmed_data <- data.frame(Gene = c("Gene1", "Gene2"), PubMed_Rank = c(1, 2))
string_data <- data.frame(Gene = c("Gene1", "Gene2"), Connectivity_Rank = c(2, 1))
combined <- combine_summary(pubmed_data, string_data, export = FALSE)
print(combined)

Run DeSciDe pipeline

Description

Run the entire analysis pipeline including PubMed search, STRING database search, and plotting.

Usage

descide(
  genes_list,
  terms_list,
  rank_method = "weighted",
  species = 9606,
  network_type = "full",
  score_threshold = 400,
  threshold_percentage = 20,
  export = FALSE,
  file_directory = NULL,
  export_format = "csv"
)

Arguments

Value

A list containing the PubMed search results, STRING results, and summary results.

Examples

genes <- c("TP53", "BRCA1")
terms <- c("cancer", "tumor")
results <- descide(genes, terms, export = FALSE)
str(results)

Plot STRING Interactions

Description

Plot STRING interactions degree vs. clustering.

Usage

plot_clustering(string_results, file_directory = NULL, export = FALSE)

Arguments

Value

Invisibly returns the ggplot object.

Examples

# Example data frame
string_results <- data.frame(Degree = c(10, 5), Clustering_Coefficient_Percent = c(20, 10))
plot_clustering(string_results, file_directory = tempdir(), export = FALSE)

Plot Connectivity vs. Precedence

Description

Create a scatter plot of Connectivity Rank vs. PubMed Rank.

Usage

plot_connectivity_precedence(
  combined_summary,
  file_directory = NULL,
  export = FALSE
)

Arguments

Value

Invisibly returns a ggplot object.

Examples

combined_data <- data.frame(Gene = c("Gene1", "Gene2"), Connectivity_Rank = c(1, 2),
                            PubMed_Rank = c(2, 1),
                            Category = c("High Connectivity - High Precedence", "Other"))
plot_connectivity_precedence(combined_data, export = FALSE)

Plot Heatmap

Description

Create and optionally save a heatmap of the PubMed search results.

Usage

plot_heatmap(pubmed_search_results, file_directory = NULL, export = FALSE)

Arguments

Value

Invisibly returns a HeatmapList object.

Examples

# Example data frame
data <- data.frame(Gene = c("Gene1", "Gene2"),
                   Term1 = c(10, 20),
                   Term2 = c(5, 15),
                   Total = c(15, 35),
                   PubMed_Rank = c(1, 2))
plot_heatmap(data, file_directory = tempdir(), export = FALSE)

Plot STRING Network

Description

Plot STRING network interactions using STRINGdb.

Usage

plot_string_network(
  string_db,
  string_ids,
  file_directory = NULL,
  export = FALSE
)

Arguments

Value

Invisibly returns NULL.

Examples

library(STRINGdb)
string_db <- STRINGdb$new(species = 9606)
string_ids <- c("9606.ENSP00000269305", "9606.ENSP00000357940")
plot_string_network(string_db, string_ids, file_directory = tempdir(), export = FALSE)

Rank Search Results

Description

Rank search results based on a chosen method.

Usage

rank_search_results(data, terms_list, rank_method = "weighted")

Arguments

Value

A data frame with ranked search results, which includes the genes and their corresponding ranks based on the search method.

Examples

# Example data frame
data <- data.frame(Gene = c("Gene1", "Gene2"),
                   Term1 = c(10, 20),
                   Term2 = c(5, 15))
terms_list <- c("Term1", "Term2")
ranked_results <- rank_search_results(data, terms_list, rank_method = "weighted")
print(ranked_results)

Search PubMed with Multiple Genes and Terms

Description

Perform a PubMed search for multiple genes and terms.

Usage

search_pubmed(genes_list, terms_list, rank_method = "weighted", verbose = TRUE)

Arguments

Value

A data frame with search results, including genes, terms, and their corresponding publication counts and ranks.

Examples

genes <- c("TP53", "BRCA1")
terms <- c("cancer", "tumor")
search_results <- search_pubmed(genes, terms, rank_method = "weighted", verbose = FALSE)
print(search_results)

Search STRING Database

Description

Search the STRING database for protein interactions.

Usage

search_string_db(
  genes_list,
  species = 9606,
  network_type = "full",
  score_threshold = 400
)

Arguments

Value

A list containing the following elements:

string_results

A data frame with STRING interaction metrics.

string_db

The STRINGdb object used.

string_ids

The STRING IDs for the input genes.

Examples

## Not run: 
library(STRINGdb)
genes <- c("TP53", "BRCA1")
results <- search_string_db(genes)
print(results)

## End(Not run)

Search PubMed

Description

Perform a PubMed search for a given gene and term.

Usage

single_pubmed_search(gene, term)

Arguments

Value

An integer representing the number of PubMed articles found from the search query in PubMed.

Examples

# Perform a PubMed search for gene 'TP53' with term 'cancer'
result <- single_pubmed_search("TP53", "cancer")
print(result)