ComplexMap is an R package that provides a complete, end-to-end workflow for the analysis of protein complex datasets. It is designed to take a raw list of putative protein complexes and transform it into a fully annotated, publication-ready functional map.
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Integrated Workflow: Go from a raw complex list to a final network map with a single function,
createComplexMap(). -
Diversity-Focused Refinement: Defaults to a conservative Jaccard similarity metric to merge only highly redundant complexes, preserving biologically meaningful variants like sub-complexes. Other metrics (Matching Score, Simpson, or Dice) are available for more aggressive merging.
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Functional Enrichment: Annotate complexes using gene sets from local files, MSigDB, Gene Ontology, or Reactome.
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Powerful Visualizations: Generate static (
ggplot2) and interactive (visNetwork) plots to explore the functional landscape. -
Quantitative Data Integration: Map your own experimental data (e.g., protein abundance, fold-change) directly onto network nodes as a continuous color gradient in any of the visualization functions.
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Robust Benchmarking: Optimize refinement parameters with
benchmarkParameters()and evaluate results against a reference standard like CORUM usingevaluateComplexes().
You can install the stable version of ComplexMap from GitHub. The installer will automatically handle all required dependencies from CRAN and Bioconductor.
# If you don't have remotes installed: install.packages("remotes")
remotes::install_github("zqzneptune/ComplexMap")This example demonstrates the core workflow, from loading data to visualization.
library(ComplexMap)
library(dplyr)
# 1. Load the example complex list and a gene set file
data("demoComplexes", package = "ComplexMap")
gmt_file <- ComplexMap::getExampleGmt()
gmt <- ComplexMap::getGmtFromFile(gmt_file, verbose = FALSE)
# 2. Run the entire workflow
# The default similarityMethod is "jaccard" to preserve diversity.
cm_obj <- ComplexMap::createComplexMap(
complexList = demoComplexes,
gmt = gmt,
mergeThreshold = 0.9, # Strict default
verbose = FALSE # Set to TRUE to see workflow steps
)
# 3. Print the resulting object for a high-level summary
cm_obj
#> # ComplexMap Object (Physical-First Layout)
#> # ── Physical Structure: 375 nodes, 893 edges (2.38 edges/node)
#> # ── Functional Landscape:
#> # • Diversity: 21 distinct functional domains (colors)
#> # • Coverage: 70.1% of complexes annotated
#> # ── Accessors: `getNodeTable()`, `getEdgeTable()`
#> # ── Analysis: `summarizeThemes()` to identify physical machines.
# 4. Visualize the map
node_tbl <- ComplexMap::getNodeTable(cm_obj)
edge_tbl <- ComplexMap::getEdgeTable(cm_obj)
ComplexMap::visualizeMapWithLegend(node_tbl, edge_tbl)
The ComplexMap object can be easily analyzed with downstream functions.
Identify the major biological themes (network modules) using community detection and add them directly to the map object for easy querying.
cm_obj <- ComplexMap::summarizeThemes(cm_obj, verbose = FALSE)
# Show the 10 largest themes by querying the object's node table
ComplexMap::getNodeTable(cm_obj) %>%
dplyr::count(themeLabel, sort = TRUE, name = "nodeCount") %>%
dplyr::slice_head(n = 10)Programmatically find specific complexes of interest.
# Find the complex with the ID "CpxMap_0001"
result <- ComplexMap::queryMap(cm_obj, query = "CpxMap_0001", type = "complex")
# Show some of its key attributes
dplyr::select(result, complexId, primaryFunctionalDomain, proteinCount, degree, themeLabel)If you use ComplexMap in your research, please cite the publication (link to be added upon publication).
For now, you can cite the package itself:
Qingzhou Zhang (2025). ComplexMap: A Toolset for the Functional Analysis and Visualization of Protein Complex Data. R package version 1.1.1. https://github.com/zqzneptune/ComplexMap
Please note that the ComplexMap project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. We welcome bug reports, feature requests, and pull requests.