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tKOI

Transcriptomic Knowledge-Graph Omics Integration for Human Pathway Analysis

The tkoi package provides an integrative framework that combines transcriptomic data with a human-specific biological knowledge graph. This enables network-aware enrichment, functional interpretation, and gene prioritization via personalized PageRank and ontology-aware annotation.

Shiny App

Launch the Shiny app from the installed R package:

tkoi::run_tkoi_app()

Install the optional app dependencies once if needed:

install.packages(c(
  "shiny", "shinythemes", "data.table", "readxl", "openxlsx", "plotly", "DT"
))

The app accepts CSV, TSV, or Excel files with gene_name, logfc, and pvalue columns. It runs the package’s analysis and lets you explore and export the results.

To serve the installed app on a network interface:

tkoi::run_tkoi_app(host = "0.0.0.0", port = 3838, launch_browser = FALSE)

The app source, tests, and deployment scripts are maintained in this repository. You can also use the hosted app.

Standalone Server Deployment

Download tkoi-shiny-1.3.0.zip or tkoi-shiny-1.3.0.tar.gz from the GitHub releases, extract it, and run these commands from the extracted app directory:

Rscript install.R
Rscript run_app.R 3838 0.0.0.0

The bundle includes the complete app and the matching R package at vendor/tkoi_1.3.0.tar.gz. The installer uses that archive and downloads its other dependencies from CRAN and Bioconductor. Setup requires R 4.1 or later, a C++ compiler, and internet access for those dependencies.

For Shiny Server, place the extracted directory in its configured app directory and install the dependencies for the account running the service. For Posit Connect, publish the extracted directory as a Shiny application. app.R is the entry point. The bundle’s README and the getting started guide provide more detail.

Agent Workflows

tKOIAgent provides the tkoi-agent plugin for Codex, Claude Code, and BioRouter. Its tkoi-analysis skill guides input preparation and analysis; its tkoi-knowledge-graph skill explores the graph through a local MCP connection. Follow the plugin’s README, setup guide, and preprocessing guide.

Since version 1.3.0, run_tkoi() keeps the exact input graph in its result. Save the result and connect the plugin’s connect_analysis tool to the absolute RDS path so enrichment and traversal use that same graph:

graph = tkoi::get_analysis_graph(tkoi_result)
saveRDS(tkoi_result, "analysis.rds")
normalizePath("analysis.rds")

Keep the analysis_id returned by connect_analysis and supply it to every subsequent MCP tool call. The server rejects stale IDs or changed files rather than silently switching graphs.

Graph paths and enrichment scores support associations and hypotheses; they do not establish causality. See the agent workflow guide for the MCP tools, direct R use, and graph attribute limits.

Documentation

Please refer to Documentation for detailed documentation of this R package. The source code is on GitHub, and changes between versions are listed in NEWS.md.

Installation

To install the development version from GitHub:

# Install devtools if necessary
install.packages("devtools")

# Install tkoi
devtools::install_github("BaranziniLab/tkoi")

The former address Broccolito/tkoi redirects to BaranziniLab/tkoi. tkoi requires R 4.1 or later.

Since version 1.1.0, tkoi compiles C++ code during installation, so a compiler toolchain is required:

  • macOS: Xcode Command Line Tools (run xcode-select --install in a terminal)
  • Windows: Rtools matching your R version
  • Linux: a C++ compiler, e.g. build-essential on Debian/Ubuntu

The Bioconductor dependencies (clusterProfiler, org.Hs.eg.db) are installed automatically. If that step fails, install them first with BiocManager and then install tkoi again:

install.packages("BiocManager")
BiocManager::install(c("clusterProfiler", "org.Hs.eg.db"))

Core Features

  • Personalized PageRank propagation using transcriptomic weights
  • Permutation-based enrichment scoring for network nodes
  • Fast, multithreaded C++ engine that solves PageRank for the observed data and all permutations in batches
  • Reproducible results: set.seed() fixes the permutation null, and results are identical for any number of cores
  • Functional annotation using Gene Ontology, Disease Ontology, Cell Ontology, Reactome, and more
  • Modular and extensible S4 object design (tKOIList)
  • Export and visualization tools for enriched subnetworks
  • Side-by-side comparison with Gene Ontology enrichment from clusterProfiler, and plots built with ggplot2

Performance

The core of run_tkoi() is written in C++ (via Rcpp). Personalized PageRank for the observed data and every permutation is solved in batches of up to 16 vectors per pass over the network, using conjugate gradient on multiple threads. Two-hop neighbourhood counts use bitsets, and the degree-matched null gene sets are sampled in C++.

Measured on an Apple M2 (8 cores) with a 21,414-gene differential expression dataset (1,201 seed genes) on the full tkoi_net:

  • 10 permutations: 48.5 s with tkoi 1.0.0 vs 5.3 s with tkoi 1.1.0 (8.8 s for the first run in a session, which also builds the network matrix)
  • 100 permutations: 244.3 s with tkoi 1.0.0 vs 25.5 to 38.1 s with tkoi 1.1.0 (the range is run-to-run variation from thermal throttling)

That is about 6 to 10 times faster. A single core is still faster than tkoi 1.0.0: 10.9 s for 10 permutations with n_cores = 1.

With the same set.seed(), the permutation null gene sets on tkoi_net are identical to those of a sequential tkoi 1.0.0 run, and the statistics match tkoi 1.0.0: beta agrees to within 1e-8 and PageRank vectors to within 6e-15. The result tables are more complete than in 1.0.0: every node is reported (unannotated nodes were dropped before), the Compound FDR is adjusted over the reported human metabolites only, nodes with a constant null are reported as untestable (NaN) instead of infinitely significant, and the list of human metabolites was repaired (see NEWS.md). Rows can otherwise be ordered differently only where FDR and beta agree to 10 significant digits, because tkoi 1.1.0 orders such ties deterministically. At the default tolerance = 1e-14, the PageRank vectors are at least as accurate as those of igraph::page_rank() on tkoi_net, both per node and overall.

Loading tkoi::tkoi_net takes a few seconds and about 0.5 GB of memory the first time it is used in a session. Keeping every permutation (keep_permutations = TRUE) adds about 0.75 GB for 100 permutations. Before any PageRank is computed, run_tkoi() checks the memory the run needs against the memory available (physical memory, or a Linux container limit) and stops with advice if the run would not fit.

Getting Started

Example Workflow

This section walks you through a complete example using the tkoi package—from reading expression data, running the core network analysis, to visualizing enrichment results.

Step 1: Load Example Gene Expression Data

The tkoi package includes a small example CSV file containing simulated gene expression results. We’ll read it using data.table for performance.

library(tkoi)
library(data.table)

# Get the file path of the example expression data
file_path = system.file("extdata", "example_data.csv", package = "tkoi")

# Read the CSV file
expression_data = fread(file_path)
head(expression_data)

The file includes columns:

  • gene_name: Ensembl gene identifiers
  • logfc: log2 fold-change values
  • pvalue: associated p-values for differential expression

Rows with a missing or blank gene_name are ignored, and only the first row of a duplicated gene is used.

Step 2: Run tKOI Network Enrichment Analysis

tKOI integrates transcriptomic changes with a biological knowledge graph using a personalized PageRank algorithm. It also performs permutations to assess statistical enrichment.

set.seed(1)                       # Makes the permutation null reproducible

tkoi_result = run_tkoi(
  expression_data = expression_data,
  subnetwork = tkoi::tkoi_net,    # Predefined igraph network included with the package
  pvalue_threshold = 0.05,        # p-value filter for differential expression
  logfc_threshold = 0.25,         # Minimum log fold change
  indirect_link_threshold = 3,    # Rank first the nodes within two hops of at least 3 seed genes
  topology_similarity = 0.9,      # Similarity for selecting matched genes in permutations
  n_permutation = 100,            # Number of random permutations
  damping_factor = 0.85,          # PageRank damping factor
  maximum_iteration = 500,        # Max solver iterations per PageRank vector
  n_cores = NULL,                 # CPU threads; NULL uses all available cores
  keep_permutations = TRUE        # FALSE keeps only the null mean and SD to save memory
)

The result is an S4 object (tKOIList) that stores PageRank scores, permutation statistics, network annotations, and the exact graph supplied as subnetwork. Retrieve that graph with tkoi::get_analysis_graph(tkoi_result) for later traversal.

A few notes on the run settings:

  • indirect_link_threshold only orders the result tables: nodes within two hops of at least that many seed genes are listed first. No node is excluded.
  • n_cores only changes the speed: results are identical for any number of cores. Requests above the number of available cores (including Slurm and Linux container CPU limits) are capped, and options(tkoi.n_cores = 4) sets a default for the session.
  • keep_permutations = FALSE stores only the null mean and standard deviation instead of every permutation, which saves memory for large n_permutation.
  • The PageRank solver stops when its relative residual is at most tolerance (default 1e-14, about 55 iterations per vector on tkoi_net). A warning is raised if a PageRank vector does not converge within maximum_iteration iterations.
  • Set verbose = FALSE to silence the progress messages.

Step 3: Perform Gene Ontology (GO) Enrichment

You can extend the analysis by integrating GO term enrichment using clusterProfiler. This allows for side-by-side comparisons of ontology-based and graph-based enrichment.

tkoi_result = run_gene_enrichment(tkoi_result)

This adds a gene_enrichment_comparison slot containing GO enrichment tables and visual summaries.

Step 4: Visualize GO vs Graph Enrichment

Two visualizations are automatically generated:

Scatter Plot (All Terms) 

tkoi_result@gene_enrichment_comparison$comparison_scatter1

Scatter Plot (Faceted by GO Namespace) 

tkoi_result@gene_enrichment_comparison$comparison_scatter2

These plots compare tKOI network enrichment (beta) with gene ontology q-values.

Step 5: Visualize Differential Genes in the Network

The make_gene_exploration_plot() function highlights upregulated and downregulated genes in a scatter plot based on both experimental and network evidence.

plt1 = make_gene_exploration_plot(
  tkoi_list = tkoi_result,
  sig_color = "#F39B7FB2",
  non_sig_color = "gray"
)
plt1

Step 6: Export Gene-Level Prioritization Table

This returns a data frame containing logFC, p-values, PageRank scores, and FDRs for each gene.

gene_data = export_gene_exploration_data(tkoi_result)
head(gene_data)

Step 7: Visualize Top N Enriched Nodes

Use visualize_topn() to highlight the most significantly enriched genes, pathways, or biological concepts based on network-level statistics.

plt2 = visualize_topn(
  tkoi_list = tkoi_result,
  category = "Gene",       # Can also be "Pathway", "BiologicalProcess", etc.
  top_n = 25,
  high_color = "#FF5733",  # Strong enrichment
  low_color = "#154360"    # Moderate enrichment
)
plt2

Step 8: (Optional) Plot the Network Around an Enriched Node

Use plot_network() to draw the part of the knowledge graph that links an enriched node (for example the top biological process) to the significant genes near it. Genes are colored by log fold change, the target node is orange, and node size follows the tKOI effect size (beta). By default, the plot uses the graph stored in the analysis result.

top_term = tkoi_result@network_summary_statistics$BiologicalProcess$node_id[1]

plot_network(
  tkoi_result = tkoi_result,
  target_node_id = top_term,
  degree_expansion = 2,          # Maximum number of hops between the target and a gene
  network_layout_type = "kk"     # Also "fr", "gem", "graphopt", "lgl", or "mds"
)

plot_network() returns the plotted igraph subgraph invisibly. Pass the stored graph explicitly to traversal helpers:

graph = tkoi::get_analysis_graph(tkoi_result)
get_neighboring_nodes(top_term, degree_expansion = 1, subnetwork = graph)

Step 9: (Optional) Save the Analysis Result

Save your full analysis object for future use:

saveRDS(tkoi_result, "analysis.rds")
tkoi_result = readRDS("analysis.rds")

The RDS preserves the analysis graph alongside the results. Older results that lack a stored graph are rejected by get_analysis_graph(); rerun the analysis with its original graph rather than substituting the current package graph.

The node-level enrichment tables can also be written to an Excel workbook, one sheet per node type:

export_network_summary_statistics(tkoi_result, filename = "tkoi_network_statistics.xlsx")

S4 Object Structure

tKOIList is an S4 object returned by run_tkoi() with the following slots:

  • expression_data: Input transcriptomic measurements
  • subnetwork: The exact igraph supplied to run_tkoi(), including its vertex and edge attributes; accessed with get_analysis_graph()
  • pagerank_data: A data frame with one row per network node (row names are node IDs): node_id, the observed pagerank, and either every permutation’s PageRank (perm.1, perm.2, …) or, with keep_permutations = FALSE, only the null mean and standard deviation (null_mean, null_sd)
  • network_summary_statistics: Node-level enrichment results, a named list with one table per node type (node_id, node_type, identifier, pagerank, beta, p_value, fdr, direct_links, indirect_links, valency, and annotation columns). Every node is listed once, with missing annotation columns when no curated annotation exists; among compounds, only human metabolites are listed. FDR is adjusted within each table, and nodes whose null has no variation are untestable (NaN) and listed last
  • gene_enrichment_comparison: GO enrichment overlay and plots, added by run_gene_enrichment()
  • pvalue_threshold, logfc_threshold, topology_similarity, n_permutation, damping_factor, maximum_iteration: The settings used for the run

Annotation Resources

Built-in annotation tables support functional interpretation of the knowledge graph:

  • go_annotation, disease_annotation, celltype_annotation, anatomy_annotation
  • compound_annotation, protein_annotation, complex_annotation
  • reaction_annotation, pathway_annotation, pwgroup_annotation, etc.

Inspect them like so:

data(go_annotation)
head(go_annotation)

License

MIT + file LICENSE

Citation

Gu, W., Bellucci, G., Peetoom, B., & Baranzini, S. E. (under review). Enhanced Transcriptomics Analysis by Integration with Large-Scale Knowledge Graphs and large language models.

Contact

Wanjun Gu ORCID: 0000-0002-7342-7000