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/.

MOTIVATION: Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. RESULTS: NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell-cell signaling at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.