Package {pathfindR}

Type:	Package
Title:	Enrichment Analysis Utilizing Active Subnetworks
Version:	3.0.0
Maintainer:	Ege Ulgen <egeulgen@gmail.com>
Description:	Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. 'pathfindR' is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. 'pathfindR' also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in 'pathfindR' are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. 'pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.
License:	MIT + file LICENSE
URL:	https://egeulgen.github.io/pathfindR/, https://github.com/egeulgen/pathfindR
BugReports:	https://github.com/egeulgen/pathfindR/issues
Encoding:	UTF-8
LazyData:	true
Imports:	DBI, AnnotationDbi, doParallel, foreach, rmarkdown, ggplot2, ggraph, ggupset, ggnewscale, fpc, ggkegg (≥ 1.4.0), grDevices, httr, igraph, R.utils, msigdbr (≥ 24.1.0), knitr, Rcpp
Depends:	R (≥ 4.3.0), pathfindR.data (≥ 2.0)
Suggests:	org.Hs.eg.db, testthat (≥ 2.3.2), covr, mockery
VignetteBuilder:	knitr
RoxygenNote:	8.0.0
LinkingTo:	Rcpp
NeedsCompilation:	yes
Packaged:	2026-06-22 18:28:26 UTC; egeulgen
Author:	Ege Ulgen [cre, cph], Ozan Ozisik [aut]
Repository:	CRAN
Date/Publication:	2026-06-22 19:00:02 UTC

pathfindR: A package for Enrichment Analysis Utilizing Active Subnetworks

Description

pathfindR is a tool for active-subnetwork-oriented gene set enrichment analysis. The main aim of the package is to identify active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values then performing enrichment analyses on the identified subnetworks, discovering enriched terms (i.e. pathways, gene ontology, TF target gene sets etc.) that possibly underlie the phenotype of interest.

Details

For analysis on non-Homo sapiens organisms, pathfindR offers utility functions for obtaining organism-specific PIN data and organism-specific gene sets data.

pathfindR also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results.

Author(s)

Maintainer: Ege Ulgen egeulgen@gmail.com (ORCID) [copyright holder]

Authors:

• Ozan Ozisik ozanytu@gmail.com (ORCID)

See Also

See run_pathfindR for details on the pathfindR active-subnetwork-oriented enrichment analysis See cluster_enriched_terms for details on methods of enriched terms clustering to define clusters of biologically-related terms See score_terms for details on agglomerated score calculation for enriched terms to investigate how a gene set is altered in a given sample (or in cases vs. controls) See term_gene_heatmap for details on visualization of the heatmap of enriched terms by involved genes See create_term_gene_graph and create_term_gene_plot for details on visualizing terms and term-related genes as a graph to determine the degree of overlap between the enriched terms by identifying shared and/or distinct significant genes See UpSet_plot for details on creating an UpSet plot of the enriched terms. See get_pin_file for obtaining organism-specific PIN data and get_gene_sets_list for obtaining organism-specific gene sets data

Find the connected components among a set of "on" nodes

Description

Returns one group of node names per connected component of the subgraph induced by on_names (isolated nodes form singleton components).

Usage

.find_components_named(network, on_names)

Arguments

Value

A list of character vectors, one per component.

Find the scored subnetworks among a set of "on" nodes

Description

Find the scored subnetworks among a set of "on" nodes

Usage

.find_subnetworks(network, sc, on_names)

Arguments

Value

A list of subnetwork objects.

Compare two individuals rank-by-rank

Description

Compares the score-sorted subnetwork scores position by position; the first strict difference decides. If one score vector is a prefix of the other, the individual with more subnetworks wins.

Usage

.ga_compare(a, b)

Arguments

Value

1 if a is better, -1 if b is better, 0 if they are equivalent.

Crossover and mutation of two parent genomes

Description

With probability ga_crossover_rate the parents are recombined (uniform crossover by default); otherwise the children are empty genomes. Mutation flips each bit with probability ga_mutation_rate.

Usage

.ga_crossover_mutation(p1, p2, params, crossover_type = "UNIFORM")

Arguments

Value

A list of two child genomes (logical vectors).

Build a GA individual from a logical genome

Description

Computes the connected subnetworks induced by the on-nodes and stores their scores (sorted descending) for fast fitness comparison. The scores are obtained with the C++ component scorer over the precomputed CSR adjacency, which is the only thing the fitness comparison (.ga_compare / .ga_sort_desc) ever reads. The node membership of a genome is not materialized here — it is reconstructed once, for the best individual, at the end of .genetic_algorithm().

Usage

.ga_make_individual(rep_logical, csr_offsets, csr_nbrs, z_vec, means, stds)

Arguments

Value

A list with elements rep (the genome) and scores (the descending component scores).

Pick one index by rank-proportional (roulette) selection

Description

Pick one index by rank-proportional (roulette) selection

Usage

.ga_pick(weights, total)

Arguments

Value

An integer index into the population.

Fitness of an individual

Description

The score of its highest scoring subnetwork, or 0 when it has none.

Usage

.ga_score(ind)

Arguments

Value

A numeric score.

Sort a population from best to worst

Description

Implements the same ordering as .ga_compare() in a vectorised way: score vectors are laid out in a matrix padded with -Inf, then ordered lexicographically (descending), with the number of subnetworks as a final tie-break so that more subnetworks ranks higher.

Usage

.ga_sort_desc(pop)

Arguments

Value

The population reordered, best individual first.

Run the genetic-algorithm active subnetwork search

Description

The initial population is seeded randomly (each gene switched on with probability params$gene_init_prob); when params$start_with_all_positives is set, one individual containing all positive-z-score nodes is added. The population then evolves by rank-based selection, uniform crossover and optional mutation. Every ten generations the worst 10% of the population is replaced with fresh random individuals, and the previous best individual is preserved. The search stops after params$ga_iterations generations or once the best individual is unchanged for 50 generations.

Usage

.genetic_algorithm(network, sc, params, verbose = FALSE)

Arguments

Value

A list of subnetwork objects from the best individual found.

Run the greedy active subnetwork search

Description

Run the greedy active subnetwork search

Usage

.greedy_search(network, score_context, params, verbose = FALSE)

Arguments

Value

A list of subnetwork objects.

Create a subnetwork object from a vector of node names

Description

Create a subnetwork object from a vector of node names

Usage

.make_subnetwork(sc, nodes)

Arguments

Value

A list with elements nodes, zsum and score.

Parse the experiment input into a clean gene / p-value data frame

Description

Accepts a data frame. If columns named gene and pvalue (case-insensitive) exist they are used, otherwise the first two columns are taken as gene and p-value respectively. Gene names are upper-cased.

Usage

.parse_experiment(experiment)

Arguments

Value

A data frame with character column gene and numeric column pvalue.

Score a subnetwork from its size and z-score sum

Description

Single-node subnetworks always score 0. Otherwise the raw score is zsum / sqrt(n), optionally calibrated against the Monte-Carlo distribution and optionally penalized for size.

Usage

.score_subnetwork(sc, n, zsum, normalize)

Arguments

Value

A numeric score.

Run the simulated annealing active subnetwork search

Description

The candidate solution starts either with all positive-z-score nodes on (when params$start_with_all_positives) or with each node switched on independently with probability params$gene_init_prob. A random node is toggled each step and the resulting subnetworks are compared rank-by-rank against the current ones, accepting the change with a temperature-dependent probability. The temperature decays geometrically from sa_initial_temp to sa_final_temp over sa_iterations steps.

Usage

.simulated_annealing(network, sc, params, verbose = FALSE)

Arguments

Details

The search loop runs in C++ (run_simulated_annealing) and reproduces the Java reference exactly: the same java.util.Random stream (seeded with params$seed), the same initial-state draw order (node order), the same nextInt-based node toggling, and the same acceptance walk over the score-ranked subnetworks (advance to the next pair when a worse move passes its Boltzmann draw; accept on a >0.001 improvement; otherwise reject).

Value

A list of subnetwork objects (all connected components of the final solution, sorted by score descending).

Sort a list of subnetwork objects by score, descending

Description

Sort a list of subnetwork objects by score, descending

Usage

.sort_subnetworks_desc(subs)

Arguments

Value

The list reordered so the highest scoring subnetwork is first.

Create UpSet Plot of Enriched Terms

Description

Create UpSet Plot of Enriched Terms

Usage

UpSet_plot(
  result_df,
  genes_df,
  num_terms = 10,
  method = "heatmap",
  use_description = FALSE,
  low = "red",
  mid = "black",
  high = "green",
  ...
)

Arguments

Value

UpSet plots are plots of the intersections of sets as a matrix. This function creates a ggplot object of an UpSet plot where the x-axis is the UpSet plot of intersections of enriched terms. By default (i.e. method = 'heatmap') the main plot is a heatmap of genes at the corresponding intersections, colored by up/down regulation (if genes_df is provided, colored by change values). If method = 'barplot', the main plot is bar plots of the number of genes at the corresponding intersections. Finally, if method = 'boxplot' and if genes_df is provided, then the main plot displays the boxplots of change values of the genes at the corresponding intersections.

Examples

UpSet_plot(example_pathfindR_output)

Wrapper for Active Subnetwork Search + Enrichment over Single/Multiple Iteration(s)

Description

Wrapper for Active Subnetwork Search + Enrichment over Single/Multiple Iteration(s)

Usage

active_snw_enrichment_wrapper(
  input_processed,
  pin_path,
  gset_list,
  enrichment_threshold,
  list_active_snw_genes,
  adj_method = "bonferroni",
  search_method = "GR",
  disable_parallel = FALSE,
  start_with_all_positives = FALSE,
  iterations = 10,
  n_processes = NULL,
  score_quan_thr = 0.8,
  sig_gene_thr = 0.02,
  sa_initial_temp = 1,
  sa_final_temp = 0.01,
  sa_iterations = 10000,
  ga_population_size = 400,
  ga_iterations = 200,
  ga_crossover_rate = 1,
  ga_mutation_rate = 0,
  gr_max_depth = 1,
  gr_search_depth = 1,
  gr_overlap_threshold = 0.5,
  gr_subnetwork_num = 1000,
  verbose = FALSE
)

Arguments

Value

Data frame of combined pathfindR enrichment results

Active subnetwork search

Description

Searches a molecular interaction network for connected subnetworks of genes that are jointly enriched for low experimental p-values ("active modules"). Gene p-values are converted to z-scores, subnetwork scores are calibrated against a Monte-Carlo background, and one of three search strategies is used to find high-scoring connected subnetworks.

Usage

active_subnetwork_search(
  network,
  score_context,
  method = c("GR", "SA", "GA"),
  params,
  verbose = FALSE
)

Arguments

Value

A list of subnetwork objects sorted by score descending, each with elements nodes (character vector of gene names) and score. The list is empty when no positive-scoring subnetwork is found.

See Also

get_active_subnetworks which builds network, score_context and params and calls this function.

Examples

## Not run: 
# Write a minimal SIF file
sif <- data.frame(
  source = c("A", "A", "B", "C", "D"),
  type   = "interacts",
  target = c("B", "C", "C", "D", "E")
)
sif_path <- tempfile(fileext = ".sif")
write.table(sif, sif_path,
  sep = "\t", row.names = FALSE, col.names = FALSE,
  quote = FALSE
)

experiment <- data.frame(
  gene   = c("A", "B", "C", "D", "E"),
  pvalue = c(0.001, 0.002, 0.001, 0.5, 0.6)
)

params <- list(
  start_with_all_positives = FALSE,
  gene_init_prob           = 0.1,
  p_for_nonsignificant     = 0.5,
  sa_initial_temp          = 1.0,
  sa_final_temp            = 0.01,
  sa_iterations            = 10000L,
  ga_population_size       = 400L,
  ga_iterations            = 200L,
  ga_crossover_rate        = 1,
  ga_mutation_rate         = 0,
  gr_max_depth             = 1L,
  gr_search_depth          = 1L,
  gr_overlap_threshold     = 0.5,
  gr_subnetwork_num        = 1000,
  seed                     = 1234L
)

network <- build_network(sif_path)
sc <- build_score_context(network, experiment, params)
snws <- active_subnetwork_search(network, sc, method = "GR", params = params)

## End(Not run)

Annotate the Affected Genes in the Provided Enriched Terms

Description

Function to annotate the involved affected (input) genes in each term.

Usage

annotate_term_genes(
  result_df,
  input_processed,
  genes_by_term = pathfindR.data::kegg_genes
)

Arguments

Value

The original data frame with two additional columns:

Up_regulated

the up-regulated genes in the input involved in the given term's gene set, comma-separated

Down_regulated

the down-regulated genes in the input involved in the given term's gene set, comma-separated

Examples

example_gene_data <- example_pathfindR_input
colnames(example_gene_data) <- c("GENE", "CHANGE", "P_VALUE")

annotated_result <- annotate_term_genes(
  result_df = example_pathfindR_output,
  input_processed = example_gene_data
)

Build the undirected interaction network from a SIF file

Description

Reads a Simple Interaction Format (SIF) file and converts it into an undirected igraph graph object. Following the Java reference's SIFReader, the column count is taken from the first line: a 2-column file uses columns 1 and 2 as the interacting nodes, a 3-column file uses columns 1 and 3 (the middle interaction-type column is ignored). Node names are upper-cased and self-interactions are discarded.

Usage

build_network(sif_path)

Arguments

Details

To reproduce the Java implementation's greedy search bit-for-bit, this function also reconstructs two order-sensitive structures that the Java code derives from its HashMap/HashSet traversal: * nodes: the node order of Java's networkNodeList (adjacency.keySet() iteration order), via java_node_order(). * nbr_idx: per-node neighbour lists in Java's HashSet iteration order, via java_neighbour_order(). These orders drive both the Monte-Carlo calibration (which shuffles z-scores in node order) and the greedy expansion/removal, so matching them is what makes the R/C++ output align with the Java reference. The igraph object is retained for the SA / GA algorithms, whose component scoring is order-independent.

Value

A list with elements: g (an igraph graph), nodes (node names in Java networkNodeList order), nbr (named list of neighbour-name vectors in Java HashSet order), nbr_idx (list of 1-based neighbour-id vectors aligned to nodes, in Java HashSet order, ready for run_greedy_search()) and name2id (named integer vector mapping node name to its index in nodes) and csr_offsets / csr_nbrs (a compressed sparse-row, 0-based adjacency used by the SA / GA component scorer).

Build the score context

Description

Computes per-node z-scores from the experiment p-values and the Monte-Carlo mean / standard deviation of the raw subnetwork score at every possible subnetwork size, used to calibrate (normalise) scores.

Usage

build_score_context(network, experiment, params)

Arguments

Details

The Monte-Carlo step is an exact replica of the Java reference's ScoreCalculations.calculateMeanAndStdForMonteCarlo: it shuffles the z-score vector 2000 times with a java.util.Random-equivalent generator and Fisher-Yates shuffle (see get_java_mc_calibration() in ‘score_calculation_utils.cpp’). Because Collections.shuffle starts from networkNodeList order, the z-score vector MUST be in Java node order; network$nodes (from build_network()) provides exactly that, so z is built over it directly. With the matching seed this reproduces the Java means/stds to floating-point precision, which is what aligns the calibrated scores and rankings with the Java output.

P-values are clamped to a safe range, the smallest p-value is kept when a gene appears more than once, and network nodes without a p-value are given params$p_for_nonsignificant.

Value

A list with elements z (named z-score vector), means and stds (numeric vectors indexed by subnetwork size), and nodes.

Cluster Enriched Terms

Description

Cluster Enriched Terms

Usage

cluster_enriched_terms(
  enrichment_res,
  method = "hierarchical",
  plot_clusters_graph = TRUE,
  use_description = FALSE,
  use_active_snw_genes = FALSE,
  ...
)

Arguments

Value

a data frame of clustering results. For 'hierarchical', the cluster assignments (Cluster) and whether the term is representative of its cluster (Status) is added as columns. For 'fuzzy', terms that are in multiple clusters are provided for each cluster. The cluster assignments (Cluster) and whether the term is representative of its cluster (Status) is added as columns.

See Also

See hierarchical_term_clustering for hierarchical clustering of enriched terms. See fuzzy_term_clustering for fuzzy clustering of enriched terms. See cluster_graph_vis for graph visualization of clustering.

Examples

example_clustered <- cluster_enriched_terms(
  example_pathfindR_output[1:3, ],
  plot_clusters_graph = FALSE
)
example_clustered <- cluster_enriched_terms(
  example_pathfindR_output[1:3, ],
  method = "fuzzy", plot_clusters_graph = FALSE
)

Graph Visualization of Clustered Enriched Terms

Description

Graph Visualization of Clustered Enriched Terms

Usage

cluster_graph_vis(
  clu_obj,
  kappa_mat,
  enrichment_res,
  kappa_threshold = 0.35,
  use_description = FALSE,
  vertex.label.cex = 0.7,
  vertex.size.scaling = 2.5
)

Arguments

Value

Plots a graph diagram of clustering results. Each node is an enriched term from 'enrichment_res'. Size of node corresponds to -log(lowest_p). Thickness of the edges between nodes correspond to the kappa statistic between the two terms. Color of each node corresponds to distinct clusters. For fuzzy clustering, if a term is in multiple clusters, multiple colors are utilized.

Examples

## Not run: 
cluster_graph_vis(clu_obj, kappa_mat, enrichment_res)

## End(Not run)

Color hsa KEGG pathway

Description

Color hsa KEGG pathway

Usage

color_kegg_pathway(
  pw_id,
  change_vec,
  scale_vals = TRUE,
  node_cols = NULL,
  legend.position = "top"
)

Arguments

Value

a ggplot object containing the colored KEGG pathway diagram visualization

Examples

## Not run: 
pw_id <- "hsa00010"
change_vec <- c(-2, 4, 6)
names(change_vec) <- c("hsa:2821", "hsa:226", "hsa:229")
result <- pathfindR:::color_kegg_pathway(pw_id, change_vec)

## End(Not run)

Combine 2 pathfindR Results

Description

Combine 2 pathfindR Results

Usage

combine_pathfindR_results(result_A, result_B, plot_common = TRUE)

Arguments

Value

Data frame of combined pathfindR enrichment results. Columns are:

ID

ID of the enriched term

Term_Description

Description of the enriched term

Fold_Enrichment_A

Fold enrichment value for the enriched term (Calculated using ONLY the input genes)

occurrence_A

the number of iterations that the given term was found to enriched over all iterations

lowest_p_A

the lowest adjusted-p value of the given term over all iterations

highest_p_A

the highest adjusted-p value of the given term over all iterations

Up_regulated_A

the up-regulated genes in the input involved in the given term's gene set, comma-separated

Down_regulated_A

the down-regulated genes in the input involved in the given term's gene set, comma-separated

Fold_Enrichment_B

Fold enrichment value for the enriched term (Calculated using ONLY the input genes)

occurrence_B

the number of iterations that the given term was found to enriched over all iterations

lowest_p_B

the lowest adjusted-p value of the given term over all iterations

highest_p_B

the highest adjusted-p value of the given term over all iterations

Up_regulated_B

the up-regulated genes in the input involved in the given term's gene set, comma-separated

Down_regulated_B

the down-regulated genes in the input involved in the given term's gene set, comma-separated

combined_p

the combined p value (via Fisher's method)

status

whether the term is found in both analyses ('common'), found only in the first ('A only') or found only in the second ('B only)

By default, the function also displays the term-gene graph of the common terms

Examples

combined_results <- combine_pathfindR_results(example_pathfindR_output, example_comparison_output)

Combined Results Graph

Description

Combined Results Graph

Usage

combined_results_graph(
  combined_df,
  selected_terms = "common",
  use_description = FALSE,
  layout = "stress",
  node_size = "num_genes"
)

Arguments

Value

a ggraph object containing the combined term-gene graph. Each node corresponds to an enriched term (orange if common, different shades of blue otherwise), an up-regulated gene (green), a down-regulated gene (red) or a conflicting (i.e. up in one analysis, down in the other or vice versa) gene (gray). An edge between a term and a gene indicates that the given term involves the gene. Size of a term node is proportional to either the number of genes (if node_size = 'num_genes') or the -log10(lowest p value) (if node_size = 'p_val').

Examples

combined_results <- combine_pathfindR_results(
  example_pathfindR_output,
  example_comparison_output,
  plot_common = FALSE
)
g <- combined_results_graph(combined_results, selected_terms = sample(combined_results$ID, 3))

Create HTML Report of pathfindR Results

Description

Create HTML Report of pathfindR Results

Usage

create_HTML_report(input, input_processed, final_res, dir_for_report)

Arguments

Create Kappa Statistics Matrix

Description

Create Kappa Statistics Matrix

Usage

create_kappa_matrix(
  enrichment_res,
  use_description = FALSE,
  use_active_snw_genes = FALSE
)

Arguments

Value

a matrix of kappa statistics between each term in the enrichment results.

Examples

sub_df <- example_pathfindR_output[1:3, ]
create_kappa_matrix(sub_df)

Create Term-Gene Graph

Description

Create Term-Gene Graph

Usage

create_term_gene_graph(
  result_df,
  genes_df = NULL,
  order_by = "lowest_p",
  term_size = "num_genes",
  term_fill = NULL,
  num_terms = 10,
  use_description = FALSE,
  use_edge_weights = FALSE
)

Arguments

Details

This function constructs an igraph object from pathfindR output, creating a network that connects enriched biological terms to their involved genes. By default, the graph connects term nodes to up-regulated genes and down-regulated genes. The size of term nodes can be adjusted by either the number of significant genes (‘term_size = ’num_genes'') or by the statistical significance (‘term_size = ’p_val'', using -log10(lowest p value)).

When 'genes_df' is provided, gene nodes contain values and not mere up/down binary values, allowing visualization of expression direction and magnitude. When 'term_fill' is supplied, term nodes obtain values enabling simultaneous visualization of pathway enrichment strength.

Setting 'use_edge_weights = TRUE' highlights hub genes by weighting edges based on how many terms a gene participates in, similar to an Up-Set plot but in a graph context. The 'num_terms' parameter controls how many top enriched terms are included (default: top 10), and 'order_by' determines the ordering criterion for term selection. The resulting igraph object can be visualized using create_term_gene_plot.

Value

A igraph object

Examples

# Normal gene-term with up/down regulated genes
g <- create_term_gene_graph(
  result_df = example_pathfindR_output
)
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  num_terms = 5
)
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  term_size = "p_val"
)

# Coloring the term nodes
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  term_fill = "Fold_Enrichment"
)

# Adding edge weights
g <- create_term_gene_graph(
  result_df = example_pathfindR_output,
  term_fill = "Fold_Enrichment",
  use_edge_weights = TRUE
)

Create Term-Gene Plot

Description

Create Term-Gene Plot

Usage

create_term_gene_plot(
  graph,
  layout = "stress",
  gene_node_fill = c("#7E2795", "white", "#27AE60"),
  term_node_fill = c("#CCBB44", "white", "#4477AA"),
  gene_node_color = c("green", "red"),
  term_node_color = "#E5D7BF",
  term_fill_label = NULL,
  term_size_label = NULL
)

Arguments

Details

This function creates a visualization of the term-gene graph (adapted from the Gene-Concept network visualization in the enrichplot package). It displays which input genes are involved in enriched biological terms, showing connections between genes and pathway/terms nodes. The graph facilitates investigation of multi-term relationships and identifies shared versus distinct genes across enriched terms.

Node coloring depends on the inputs provided to create_term_gene_graph:

• If 'genes_df' was NOT supplied: term nodes are beige ('term_node_color'), up-regulated genes are green, and down-regulated genes are red.

• If 'genes_df' WAS supplied: gene nodes are colored by logFC using a gradient ('gene_node_fill': default purple → white → green), and term nodes can be colored by 'term_fill' values (default yellow → white → blue) if 'term_fill' was provided.

Term node size reflects either the number of associated genes (‘term_size = ’num_genes'‘) or statistical significance ('term_size = ’p_val''). When 'use_edge_weights = TRUE' was set in 'create_term_gene_graph', edge widths represent hub gene importance (genes appearing in multiple terms). The layout can be customized via the 'layout' parameter (default: "stress"), and legends automatically reflect the applied coloring schemes.

Value

A ggraph object

Examples

# Normal gene-term with up/down regulated genes
g <- create_term_gene_graph(
  result_df = example_pathfindR_output
)
plt <- create_term_gene_plot(g)

Perform Enrichment Analysis for a Single Gene Set

Description

Perform Enrichment Analysis for a Single Gene Set

Usage

enrichment(
  input_genes,
  genes_by_term = pathfindR.data::kegg_genes,
  term_descriptions = pathfindR.data::kegg_descriptions,
  adj_method = "bonferroni",
  enrichment_threshold = 0.05,
  sig_genes_vec,
  background_genes
)

Arguments

Value

A data frame that contains enrichment results

See Also

p.adjust for adjustment of p values. See run_pathfindR for the wrapper function of the pathfindR workflow. hyperg_test for the details on hypergeometric distribution-based hypothesis testing.

Examples

enrichment(
  input_genes = c("PER1", "PER2", "CRY1", "CREB1"),
  sig_genes_vec = "PER1",
  background_genes = unlist(pathfindR.data::kegg_genes)
)

Perform Enrichment Analyses on the Input Subnetworks

Description

Perform Enrichment Analyses on the Input Subnetworks

Usage

enrichment_analyses(
  snws,
  sig_genes_vec,
  pin_name_path = "Biogrid",
  genes_by_term = pathfindR.data::kegg_genes,
  term_descriptions = pathfindR.data::kegg_descriptions,
  adj_method = "bonferroni",
  enrichment_threshold = 0.05,
  list_active_snw_genes = FALSE
)

Arguments

Value

a dataframe of combined enrichment results. Columns are:

ID

ID of the enriched term

Term_Description

Description of the enriched term

Fold_Enrichment

Fold enrichment value for the enriched term

p_value

p value of enrichment

adj_p

adjusted p value of enrichment

support

the support (proportion of active subnetworks leading to enrichment over all subnetworks) for the gene set

non_Signif_Snw_Genes (OPTIONAL)

the non-significant active subnetwork genes, comma-separated

See Also

enrichment for the enrichment analysis for a single gene set

Examples

enr_res <- enrichment_analyses(
  snws = example_active_snws[1:2],
  sig_genes_vec = example_pathfindR_input$Gene.symbol[1:25],
  pin_name_path = "KEGG"
)

Create Bubble Chart of Enrichment Results

Description

This function is used to create a ggplot2 bubble chart displaying the enrichment results.

Usage

enrichment_chart(
  result_df,
  top_terms = 10,
  plot_by_cluster = FALSE,
  num_bubbles = 4,
  even_breaks = TRUE,
  order_by = "lowest_p"
)

Arguments

Value

a ggplot2 object containing the bubble chart. The x-axis corresponds to fold enrichment values while the y-axis indicates the enriched terms. Size of the bubble indicates the number of significant genes in the given enriched term. Color indicates the -log10(lowest-p) value. The closer the color is to red, the more significant the enrichment is. Optionally, if 'Cluster' is a column of result_df and plot_by_cluster == TRUE, the enriched terms are grouped by clusters.

Examples

g <- enrichment_chart(example_pathfindR_output)

Example Unfiltered Active Subnetworks

Description

List containing unfiltered active subnetworks returned by active_subnetwork_search

Usage

example_unfiltered_snws

Format

A list with 1000 elements. Each containing a named list:

nodes

active subnetwork nodes

score

score of the active subnetwork

Fetch Gene Set Objects

Description

Function for obtaining the gene sets per term and the term descriptions to be used for enrichment analysis.

Usage

fetch_gene_sets(
  gene_sets = "KEGG",
  min_gset_size = 10,
  max_gset_size = 300,
  custom_genes = NULL,
  custom_descriptions = NULL
)

Arguments

Value

a list containing 2 elements

genes_by_term

list of vectors of genes contained in each term

term_descriptions

vector of descriptions per each term

Examples

KEGG_gset <- fetch_gene_sets()
GO_MF_gset <- fetch_gene_sets("GO-MF", min_gset_size = 20, max_gset_size = 100)

Parse Active Subnetwork Search Output File and Filter the Subnetworks

Description

Parse Active Subnetwork Search Output File and Filter the Subnetworks

Usage

filter_active_subnetworks(
  active_snws,
  sig_genes_vec,
  score_quan_thr = 0.8,
  sig_gene_thr = 0.02
)

Arguments

Value

A list containing subnetworks: a list of of genes in every active subnetwork that has a score greater than the score_quan_thrth quantile and that contains at least sig_gene_thr of significant genes and scores the score of each filtered active subnetwork

See Also

See run_pathfindR for the wrapper function of the pathfindR enrichment workflow

Examples

filtered <- filter_active_subnetworks(
  active_snws = example_unfiltered_snws,
  sig_genes_vec = example_pathfindR_input$Gene.symbol
)

Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms

Description

Heuristic Fuzzy Multiple-linkage Partitioning of Enriched Terms

Usage

fuzzy_term_clustering(
  kappa_mat,
  enrichment_res,
  kappa_threshold = 0.35,
  use_description = FALSE
)

Arguments

Details

The fuzzy clustering algorithm was implemented based on: Huang DW, Sherman BT, Tan Q, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007;8(9):R183.

Value

a boolean matrix of cluster assignments. Each row corresponds to an enriched term, each column corresponds to a cluster.

Examples

## Not run: 
fuzzy_term_clustering(kappa_mat, enrichment_res)
fuzzy_term_clustering(kappa_mat, enrichment_res, kappa_threshold = 0.45)

## End(Not run)

Get Active Subnetworks

Description

Performs active subnetwork search and filters identified active subnetworks before returning final subnetworks.

Usage

get_active_subnetworks(
  significant_genes,
  network,
  score_context,
  score_quan_thr = 0.8,
  sig_gene_thr = 0.02,
  search_method = "GR",
  seed_for_stochastic_methods = 1234,
  verbose = FALSE,
  start_with_all_positives = FALSE,
  gene_init_prob = 0.1,
  sa_initial_temp = 1,
  sa_final_temp = 0.01,
  sa_iterations = 10000,
  ga_population_size = 400,
  ga_iterations = 200,
  ga_crossover_rate = 1,
  ga_mutation_rate = 0,
  gr_max_depth = 1,
  gr_search_depth = 1,
  gr_overlap_threshold = 0.5,
  gr_subnetwork_num = 1000
)

Arguments

Value

A list of genes in every identified active subnetwork that has a score greater than the 'score_quan_thr'th quantile and that has at least 'sig_gene_thr' affected genes.

Examples

experiment_df <- example_pathfindR_input[1:15, c(1, 3)]
colnames(experiment_df) <- c("gene", "pvalue")
pin_path <- return_pin_path("KEGG")
network <- build_network(pin_path)
score_context <- build_score_context(
  network,
  experiment_df,
  list(p_for_nonsignificant = 0.5, seed = 1234L)
)
GR_snws <- get_active_subnetworks(
  significant_genes = experiment_df$gene,
  network = network,
  score_context = score_context
)

Retrieve the Requested Release of Organism-specific BioGRID PIN

Description

Retrieve the Requested Release of Organism-specific BioGRID PIN

Usage

get_biogrid_pin(org = "Homo_sapiens", path2pin, release = "latest")

Arguments

Value

the path of the file in which the PIN data was saved. If path2pin was not supplied by the user, the PIN data is saved in a temporary file

Retrieve Organism-specific Gene Sets List

Description

Retrieve Organism-specific Gene Sets List

Usage

get_gene_sets_list(
  source = "KEGG",
  org_code = "hsa",
  species = "Homo sapiens",
  db_species = "HS",
  collection,
  subcollection = NULL
)

Arguments

Value

A list containing 2 elements:

• gene_sets - A list containing the genes involved in each gene set

• descriptions - A named vector containing the descriptions for each gene set

. For 'KEGG' and 'MSigDB', it is possible to choose a specific organism. For a full list of all available KEGG organisms, see https://www.genome.jp/kegg/catalog/org_list.html. See msigdbr_species for all the species available in the msigdbr package used for obtaining 'MSigDB' gene sets. For Reactome, there is only one collection of pathway gene sets.

Retrieve Organism-specific KEGG Pathway Gene Sets

Description

Retrieve Organism-specific KEGG Pathway Gene Sets

Usage

get_kegg_gsets(org_code = "hsa")

Arguments

Value

list containing 2 elements:

• gene_sets - A list containing KEGG IDs for the genes involved in each KEGG pathway

• descriptions - A named vector containing the descriptions for each KEGG pathway

Retrieve Organism-specific MSigDB Gene Sets

Description

Retrieve Organism-specific MSigDB Gene Sets

Usage

get_mgsigdb_gsets(
  species = "Homo sapiens",
  db_species = "HS",
  collection = NULL,
  subcollection = NULL
)

Arguments

Details

this function utilizes the function msigdbr from the msigdbr package to retrieve the 'Molecular Signatures Database' (MSigDB) gene sets (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>, Liberzon et al. 2015 <doi:10.1016/j.cels.2015.12.004>). Available collections are: H: hallmark gene sets, C1: positional gene sets, C2: curated gene sets, C3: motif gene sets, C4: computational gene sets, C5: GO gene sets, C6: oncogenic signatures and C7: immunologic signatures

Value

Retrieves the MSigDB gene sets and returns a list containing 2 elements:

• gene_sets - A list containing the genes involved in each of the selected MSigDB gene sets

• descriptions - A named vector containing the descriptions for each selected MSigDB gene set

Retrieve Organism-specific PIN data

Description

Retrieve Organism-specific PIN data

Usage

get_pin_file(source = "BioGRID", org = "Homo_sapiens", path2pin, ...)

Arguments

Value

the path of the file in which the PIN data was saved. If path2pin was not supplied by the user, the PIN data is saved in a temporary file

Examples

## Not run: 
pin_path <- get_pin_file()

## End(Not run)

Retrieve Reactome Pathway Gene Sets

Description

Retrieve Reactome Pathway Gene Sets

Usage

get_reactome_gsets()

Value

Gets the latest Reactome pathways gene sets in gmt format. Parses the gmt file and returns a list containing 2 elements:

• gene_sets - A list containing the genes involved in each Reactome pathway

• descriptions - A named vector containing the descriptions for each Reactome pathway

Retrieve Gene Sets from GMT-format File

Description

Retrieve Gene Sets from GMT-format File

Usage

gset_list_from_gmt(path2gmt, descriptions_idx = 2)

Arguments

Value

list containing 2 elements:

• gene_sets - A list containing the genes involved in each gene set

• descriptions - A named vector containing the descriptions for each gene set

Hierarchical Clustering of Enriched Terms

Description

Hierarchical Clustering of Enriched Terms

Usage

hierarchical_term_clustering(
  kappa_mat,
  enrichment_res,
  num_clusters = NULL,
  use_description = FALSE,
  clu_method = "average",
  plot_hmap = FALSE,
  plot_dend = TRUE
)

Arguments

Details

The function initially performs hierarchical clustering of the enriched terms in enrichment_res using the kappa statistics (defining the distance as 1 - kappa_statistic). Next, the clustering dendrogram is cut into k = 2, 3, ..., n - 1 clusters (where n is the number of terms). The optimal number of clusters is determined as the k value which yields the highest average silhouette width. (if num_clusters not specified)

Value

a vector of clusters for each enriched term in the enrichment results.

Examples

## Not run: 
hierarchical_term_clustering(kappa_mat, enrichment_res)
hierarchical_term_clustering(kappa_mat, enrichment_res, method = "complete")

## End(Not run)

Hypergeometric Distribution-based Hypothesis Testing

Description

Hypergeometric Distribution-based Hypothesis Testing

Usage

hyperg_test(term_genes, chosen_genes, background_genes)

Arguments

Details

To determine whether the chosen_genes are enriched (compared to a background pool of genes) in the term_genes, the hypergeometric distribution is assumed and the appropriate p value (the value under the right tail) is calculated and returned.

Value

the p-value as determined using the hypergeometric distribution.

Examples

hyperg_test(letters[1:5], letters[2:5], letters)
hyperg_test(letters[1:5], letters[2:10], letters)
hyperg_test(letters[1:5], letters[2:13], letters)

Process Input

Description

Process Input

Usage

input_processing(
  input,
  p_val_threshold = 0.05,
  pin_name_path = "Biogrid",
  convert2alias = TRUE
)

Arguments

Value

This function first filters the input so that all p values are less than or equal to the threshold. Next, gene symbols that are not found in the PIN are identified. If aliases of these gene symbols are found in the PIN, the symbols are converted to the corresponding aliases. The resulting data frame containing the original gene symbols, the updated symbols, change values and p values is then returned.

See Also

See run_pathfindR for the wrapper function of the pathfindR workflow

Examples

processed_df <- input_processing(
  input = example_pathfindR_input[1:5, ],
  pin_name_path = "KEGG"
)
processed_df <- input_processing(
  input = example_pathfindR_input[1:5, ],
  pin_name_path = "KEGG",
  convert2alias = FALSE
)

Input Testing

Description

Input Testing

Usage

input_testing(input, p_val_threshold = 0.05)

Arguments

Value

Only checks if the input and the threshold follows the required specifications.

See Also

See run_pathfindR for the wrapper function of the pathfindR workflow

Examples

input_testing(example_pathfindR_input, 0.05)

Check if value is a valid color

Description

Check if value is a valid color

Usage

isColor(x)

Arguments

Value

TRUE if x is a valid color, otherwise FALSE

Order input data frame by provided columnn

Description

Order input data frame by provided columnn

Usage

order_df_by_columnn(df, order_by)

Arguments

Value

The ordered data frame or raises error

Plot the Heatmap of Score Matrix of Enriched Terms per Sample

Description

Plot the Heatmap of Score Matrix of Enriched Terms per Sample

Usage

plot_scores(
  score_matrix,
  cases = NULL,
  label_samples = TRUE,
  case_title = "Case",
  control_title = "Control",
  low = "green",
  mid = "black",
  high = "red"
)

Arguments

Value

A 'ggplot2' object containing the heatmap plot. x-axis indicates the samples. y-axis indicates the enriched terms. 'Score' indicates the score of the term in a given sample. If cases are provided, the plot is divided into 2 facets, named by case_title and control_title.

Examples

score_matrix <- score_terms(
  example_pathfindR_output,
  example_experiment_matrix,
  plot_hmap = FALSE
)
hmap <- plot_scores(score_matrix)

Process Data frame of Protein-protein Interactions

Description

Process Data frame of Protein-protein Interactions

Usage

process_pin(pin_df)

Arguments

Value

processed PIN data frame (removes self-interactions and duplicated interactions)

Return The Path to Given Protein-Protein Interaction Network (PIN)

Description

This function returns the absolute path/to/PIN.sif. While the default PINs are 'Biogrid', 'STRING', 'GeneMania', 'IntAct', 'KEGG' and 'mmu_STRING'. The user can also use any other PIN by specifying the 'path/to/PIN.sif'. All PINs to be used in this package must formatted as SIF files: i.e. have 3 columns with no header, no row names and be tab-separated. Columns 1 and 3 must be interactors' gene symbols, column 2 must be a column with all rows consisting of 'pp'.

Usage

return_pin_path(pin_name_path = "Biogrid")

Arguments

Value

The absolute path to chosen PIN.

See Also

See run_pathfindR for the wrapper function of the pathfindR workflow

Examples

## Not run: 
pin_path <- return_pin_path("GeneMania")

## End(Not run)

Wrapper Function for pathfindR - Active-Subnetwork-Oriented Enrichment Workflow

Description

run_pathfindR is the wrapper function for the pathfindR workflow

Usage

run_pathfindR(
  input,
  gene_sets = "KEGG",
  min_gset_size = 10,
  max_gset_size = 300,
  custom_genes = NULL,
  custom_descriptions = NULL,
  pin_name_path = "Biogrid",
  p_val_threshold = 0.05,
  enrichment_threshold = 0.05,
  convert2alias = TRUE,
  plot_enrichment_chart = TRUE,
  output_dir = NULL,
  list_active_snw_genes = FALSE,
  ...
)

Arguments

Details

This function takes in a data frame consisting of Gene Symbol, log-fold-change and adjusted-p values. After input testing, any gene symbols that are not in the PIN are converted to alias symbols if the alias is in the PIN. Next, active subnetwork search is performed. Enrichment analysis is performed using the genes in each of the active subnetworks. Terms with adjusted-p values lower than enrichment_threshold are discarded. The lowest adjusted-p value (over all subnetworks) for each term is kept. This process of active subnetwork search and enrichment is repeated for a selected number of iterations, which is done in parallel. Over all iterations, the lowest and the highest adjusted-p values, as well as number of occurrences are reported for each enriched term.

Value

Data frame of pathfindR enrichment results. Columns are:

ID

ID of the enriched term

Term_Description

Description of the enriched term

Fold_Enrichment

Fold enrichment value for the enriched term (Calculated using ONLY the input genes)

occurrence

the number of iterations that the given term was found to enriched over all iterations

support

the median support (proportion of active subnetworks leading to enrichment within an iteration) over all iterations

lowest_p

the lowest adjusted-p value of the given term over all iterations

highest_p

the highest adjusted-p value of the given term over all iterations

non_Signif_Snw_Genes (OPTIONAL)

the non-significant active subnetwork genes, comma-separated

Up_regulated

the up-regulated genes (as determined by ‘change value' > 0, if the 'change column' was provided) in the input involved in the given term’s gene set, comma-separated. If change column not provided, all affected are listed here.

Down_regulated

the down-regulated genes (as determined by ‘change value' < 0, if the 'change column' was provided) in the input involved in the given term’s gene set, comma-separated

The function also creates an HTML report with the pathfindR enrichment results linked to the visualizations of the enriched terms in addition to the table of converted gene symbols. This report can be found in 'output_dir/results.html' under the current working directory.

By default, a bubble chart of top 10 enrichment results are plotted. The x-axis corresponds to fold enrichment values while the y-axis indicates the enriched terms. Sizes of the bubbles indicate the number of significant genes in the given terms. Color indicates the -log10(lowest-p) value; the more red it is, the more significant the enriched term is. See enrichment_chart.

Warning

Especially depending on the protein interaction network, the algorithm and the number of iterations you choose, 'active subnetwork search + enrichment' component of run_pathfindR may take a long time to finish.

See Also

input_testing for input testing, input_processing for input processing, get_active_subnetworks for active subnetwork search and subnetwork filtering, enrichment_analyses for enrichment analysis (using the active subnetworks), summarize_enrichment_results for summarizing the active-subnetwork-oriented enrichment results, annotate_term_genes for annotation of affected genes in the given gene sets, visualize_terms for visualization of enriched terms, enrichment_chart for a visual summary of the pathfindR enrichment results, foreach for details on parallel execution of looping constructs, cluster_enriched_terms for clustering the resulting enriched terms and partitioning into clusters.

Examples

## Not run: 
run_pathfindR(example_pathfindR_input)

## End(Not run)

Safely download and parse web content

Description

This helper function retrieves content from a given URL using httr. It ensures that common issues (e.g. no internet, timeouts, HTTP errors, or parsing errors) are handled gracefully with clear, informative error messages.

Usage

safe_get_content(url, ..., timeout_sec = 10)

Arguments

Details

This function is intended for use inside package functions. For examples, vignettes, or tests, wrap calls in a connectivity check (e.g. using http_error(HEAD(url))) to avoid CRAN failures when the resource is temporarily unavailable.

Value

A character string containing the parsed content of the response (UTF-8 encoded). On failure, an error is raised with a clear message.

Examples

## Not run: 
# Retrieve the latest BioGRID release page
result <- safe_get_content("https://downloads.thebiogrid.org/BioGRID/Latest-Release/")

## End(Not run)

Calculate Agglomerated Scores of Enriched Terms for Each Subject

Description

Calculate Agglomerated Scores of Enriched Terms for Each Subject

Usage

score_terms(
  enrichment_table,
  exp_mat,
  cases = NULL,
  use_description = FALSE,
  plot_hmap = TRUE,
  ...
)

Arguments

Value

Matrix of agglomerated scores of each enriched term per sample. Columns are samples, rows are enriched terms. Optionally, displays a heatmap of this matrix.

Conceptual Background

For an experiment matrix (containing expression, methylation, etc. values), the rows of which are genes and the columns of which are samples, we denote:

• E as a matrix of size m x n

• G as the set of all genes in the experiment G = E_i., i ∈ [1, m]

• S as the set of all samples in the experiment S = E_.j, i ∈ [1, n]

We next define the gene score matrix GS (the standardized experiment matrix, also of size m x n) as:

where g ∈ G, s ∈ S, ē_g is the mean of all values for gene g and s_g is the standard deviation of all values for gene g.

We next denote T to be a set of terms (where each t ∈ T is a set of term-related genes, i.e., t = {g_x, ..., g_y} ⊂ G) and finally define the agglomerated term scores matrix TS (where rows correspond to genes and columns corresponds to samples s.t. the matrix has size |T| x n) as:

, where t ∈ T and s ∈ S.

Examples

score_matrix <- score_terms(
  example_pathfindR_output,
  example_experiment_matrix,
  plot_hmap = FALSE
)

Active Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteration

Description

Active Subnetwork Search + Enrichment Analysis Wrapper for a Single Iteration

Usage

single_iter_wrapper(
  i = NULL,
  pin_path,
  network,
  experiment_df,
  score_quan_thr,
  sig_gene_thr,
  search_method,
  verbose,
  start_with_all_positives,
  gene_init_prob,
  sa_initial_temp,
  sa_final_temp,
  sa_iterations,
  ga_population_size,
  ga_iterations,
  ga_crossover_rate,
  ga_mutation_rate,
  gr_max_depth,
  gr_search_depth,
  gr_overlap_threshold,
  gr_subnetwork_num,
  gset_list,
  adj_method,
  enrichment_threshold,
  list_active_snw_genes
)

Arguments

Value

Data frame of enrichment results using active subnetwork search results

Summarize Enrichment Results

Description

Summarize Enrichment Results

Usage

summarize_enrichment_results(enrichment_res, list_active_snw_genes = FALSE)

Arguments

Value

a dataframe of summarized enrichment results (over multiple iterations). Columns are:

ID

ID of the enriched term

Term_Description

Description of the enriched term

Fold_Enrichment

Fold enrichment value for the enriched term

occurrence

the number of iterations that the given term was found to enriched over all iterations

support

the median support (proportion of active subnetworks leading to enrichment within an iteration) over all iterations

lowest_p

the lowest adjusted-p value of the given term over all iterations

highest_p

the highest adjusted-p value of the given term over all iterations

non_Signif_Snw_Genes (OPTIONAL)

the non-significant active subnetwork genes, comma-separated

Examples

## Not run: 
summarize_enrichment_results(enrichment_res)

## End(Not run)

Create Terms by Genes Heatmap

Description

Create Terms by Genes Heatmap

Usage

term_gene_heatmap(
  result_df,
  genes_df,
  num_terms = 10,
  use_description = FALSE,
  low = "red",
  mid = "black",
  high = "green",
  legend_title = "change",
  sort_terms_by_p = FALSE,
  ...
)

Arguments

Value

a ggplot2 object of a heatmap where rows are enriched terms and columns are involved input genes. If genes_df is provided, colors of the tiles indicate the change values.

Examples

term_gene_heatmap(example_pathfindR_output, num_terms = 3)

Visualize Human KEGG Pathways

Description

Visualize Human KEGG Pathways

Usage

visualize_KEGG_diagram(
  kegg_pw_ids,
  input_processed,
  scale_vals = TRUE,
  node_cols = NULL,
  legend.position = "top"
)

Arguments

Value

Creates colored visualizations of the enriched human KEGG pathways and returns them as a list of ggplot objects, named by Term ID.

See Also

See visualize_terms for the wrapper function for creating enriched term diagrams. See run_pathfindR for the wrapper function of the pathfindR enrichment workflow.

Examples

## Not run: 
input_processed <- data.frame(
  GENE = c("PKLR", "GPI", "CREB1", "INS"),
  CHANGE = c(1.5, -2, 3, 5)
)
gg_list <- visualize_KEGG_diagram(c("hsa00010", "hsa04911"), input_processed)

## End(Not run)

Visualize Active Subnetworks

Description

Visualize Active Subnetworks

Usage

visualize_active_subnetworks(
  active_snws,
  genes_df,
  pin_name_path = "Biogrid",
  num_snws,
  layout = "stress",
  score_quan_thr = 0.8,
  sig_gene_thr = 0.02,
  ...
)

Arguments

Value

a list of ggplot objects of graph visualizations of identified active subnetworks. Green nodes are down-regulated genes, reds are up-regulated genes and yellows are non-input genes

Examples

# visualize top 2 active subnetworks
g_list <- visualize_active_subnetworks(
  active_snws = example_unfiltered_snws,
  genes_df = example_pathfindR_input[1:10, ],
  pin_name_path = "KEGG",
  num_snws = 2
)

Visualize Interactions of Genes Involved in the Given Enriched Terms

Description

Visualize Interactions of Genes Involved in the Given Enriched Terms

Usage

visualize_term_interactions(result_df, pin_name_path, show_legend = TRUE)

Arguments

Details

The following steps are performed for the visualization of interactions of genes involved for each enriched term:

1. shortest paths between all affected genes are determined (via igraph)

2. the nodes of all shortest paths are merged

3. the PIN is subsetted using the merged nodes (genes)

4. using the PIN subset, the graph showing the interactions is generated

5. the final graph is visualized using igraph, colored by changed status (if provided)

Value

list of ggplot objects (named by Term ID) visualizing the interactions of genes involved in the given enriched terms (annotated in the result_df) in the PIN used for enrichment analysis (specified by pin_name_path).

See Also

See visualize_terms for the wrapper function for creating enriched term diagrams. See run_pathfindR for the wrapper function of the pathfindR enrichment workflow.

Examples

## Not run: 
result_df <- example_pathfindR_output[1:2, ]
gg_list <- visualize_term_interactions(result_df, pin_name_path = "IntAct")

## End(Not run)

Create Diagrams for Enriched Terms

Description

Create Diagrams for Enriched Terms

Usage

visualize_terms(
  result_df,
  input_processed = NULL,
  is_KEGG_result = TRUE,
  pin_name_path = "Biogrid",
  ...
)

Arguments

Details

For is_KEGG_result = TRUE, KEGG pathway diagrams are created, affected nodes colored by up/down regulation status. For other gene sets, interactions of affected genes are determined (via a shortest-path algorithm) and are visualized (colored by change status) using igraph.

Value

Depending on the argument is_KEGG_result, creates visualization of interactions of genes involved in the list of enriched terms in result_df. Returns a list of ggplot objects named by Term ID.

See Also

See visualize_KEGG_diagram for the visualization function of KEGG diagrams. See visualize_term_interactions for the visualization function that generates diagrams showing the interactions of input genes in the PIN. See run_pathfindR for the wrapper function of the pathfindR workflow.

Examples

## Not run: 
input_processed <- data.frame(
  GENE = c("PARP1", "NDUFA1", "STX6", "SNAP23"),
  CHANGE = c(1.5, -2, 3, 5)
)
result_df <- example_pathfindR_output[1:2, ]

gg_list <- visualize_terms(result_df, input_processed)
gg_list2 <- visualize_terms(result_df, is_KEGG_result = FALSE, pin_name_path = "IntAct")

## End(Not run)