PathwayEmbed: A Computational Tool to Quantify Intracellular Signaling Transduction States from Transcriptomic Data

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ID: 315160
2026
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Abstract
Abstract Motivation Intracellular signaling pathways regulate essential cellular functions and orchestrate complex biological processes, yet their dynamic activity remains challenging to quantify with precision. Advances in single-cell omics enable pathway activity inference at the transcriptional level; however, existing computational tools often overlook mechanistic features of signaling networks, failing to formally treat the expected directionality of transcriptional change due to signal transduction. To address this technological gap, we have engineered PathwayEmbed, an R-based computational framework for estimating intracellular signal transduction states from single-cell transcriptomic datasets. Results PathwayEmbed integrates KEGG pathway information with perturbation-derived RNA sequencing data to assign directional coefficients that capture gene-specific transcriptional responses to pathway activation, repression, and/or signal transduction. These coefficients, in combination with the input data, are used to compute hypothetic ON/OFF range for each pathway. Each cell is then mapped to a specific location between these ON/OFF states, and activity scores are then computed based on the distances to these reference states, providing a continuous and interpretable measure of signaling activity at single-cell resolution. This framework enables robust visualization and quantitative comparison of pathway activity across cell populations. Applied to spatial transcriptomic data, PathwayEmbed captures spatial variation in signaling transduction states and allows comparisons at both temporal and spatial scale. The framework takes tabular data as input and is broadly compatible with established single-cell analysis workflows, supports user-defined pathway ground-truths, and offers a flexible, mechanistically informed approach for quantifying and comparing intracellular signaling activity in a wide variety of contexts. Availability PathwayEmbed is an open-source R software under academic free license, and it is available at https://github.com/raredonlab/PathwayEmbed. Use-case vignettes are available at https://raredonlab.github.io/PathwayEmbed/. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7162654151 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Y Huang, Sharon Gerecht, Themis Kyriakides, Micha Sam Brickman Raredon
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag346
URL
https://doi.org/10.1093/bioinformatics/btag346
Keywords Keywords not found

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