Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES
Micha Sam Brickman RaredoniD, Junchen YangiD, Neeharika KothapalliiD, Wesley LewisiD, Naftali KaminskiiD, Laura E. NiklasoniD, Yuval KlugeriD
Abstract
SummaryRecent 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. 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 and is compatible with existing single-cell packages and pseudotime techniques. NICHES is also a user friendly and extensible program, allowing rapid analysis of cell-cell signaling at single-cell resolution. Availability and implementationNICHES is an open-source software implemented in R under academic free license v3.0 and it is available at github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. Contactmichasam.raredon@yale.edu; yuval.kluger@yale.edu
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 128
- FWCI
- 7.59
- Field percentile
- top 1%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics
Suoqin Jin, Maksim V. Plikus, Qing Hua Nie·Nature ProtocolsPeer-reviewed·Sep 16, 2024
- Pathway-Centric Visualization of Cell-Cell Communication in Single-Cell Transcriptomics Data
Sidrah Maryam, Martin Manolov, … Sikander Hayat·npj Systems Biology and ApplicationsPeer-reviewed·Jun 20, 2026
- CytoSignal Detects Locations and Dynamics of Ligand-Receptor Signaling at Cellular Resolution from Spatial Transcriptomic Data
Jialin Liu, Hiroaki Manabe, … Joshua D. Welch·Nature GeneticsPeer-reviewed·Jun 1, 2026
- Decoding Hierarchical Cell-Cell Communication in Spatial Multi-Omics with CellSTIC
Shuai Wang, Jiayin Wang, … Yi Xu·bioRxiv·Jun 5, 2026
- Identifying Context-Specific Cell-Cell Interaction Genes Without Ligand-Receptor Databases from Spatial Transcriptomics
Hyobin Kim, B. J. Park, … Kyoung Jae Won·bioRxiv·May 12, 2026
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer
Xiao Xiao, Le Yuan Zhang, … Zuoheng Wang·Nature Machine IntelligencePeer-reviewed·Dec 31, 2025