ArticleNature biotechnology2024
Comparative analysis of cell-cell communication at single-cell resolution.
Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
85 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- Article
- Article
- Multi-omic characterization of 14q deletion in renal clear cell carcinoma identifiesJournal for immunotherapy of cancer · 2026Article
- Spatial Autocorrelation Aware Resampling Improves Cell-Cell Interaction Inference in Spatial Transcriptomics Data.bioRxiv : the preprint server for biology · 2026Article
- Addressing multiple facets of ligand-receptor network inference including single-cell proteomics.Nucleic acids research · 2026Article
- Article
- CytoSignal detects locations and dynamics of ligand-receptor signaling at cellular resolution from spatial transcriptomic data.Nature genetics · 2026Article
- Robust identification of cell-cell communication heterogeneity in single cells.bioRxiv : the preprint server for biology · 2026Article
- Empowering multifaceted analysis of spatial transcriptomics data with RGAST.Briefings in bioinformatics · 2026Article
- Tetrameric STAT5 regulates the formation of immune niche cells to protect stem cell regenerative repair against mucosal inflammation.Experimental & molecular medicine · 2026Article
- Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.bioRxiv : the preprint server for biology · 2026Article
- scComm: a contrastive learning framework for deciphering cell-cell communications at single-cell resolution.Genome biology · 2026Article
- Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling.Nature communications · 2026Article
- Subcellular transcriptome sequencing with single cell APEX-seq identifies regulators of cell-cell interactions.bioRxiv : the preprint server for biology · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Review
- Reprogramming of stroma-derived chemokine networks drives the loss of tissue organization in nodal B cell lymphoma.Nature cancer · 2026Article
- Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal.Bioinformatics (Oxford, England) · 2026Article
- Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.Nature communications · 2026Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
4 authors.
Funding
Abstract
Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.