ArticleNature communications2025
DECIPHER for learning disentangled cellular embeddings in large-scale heterogeneous spatial omics data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Cross-Modal Denoising and Integration of Spatial Multi-Omics Data with CANDIES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CellNiche represents cellular microenvironments in atlas-scale spatial omics data with contrastive learning.Nature communications · 2026Article
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
- DECIPHER for learning disentangled cellular embeddings in large-scale heterogeneous spatial omics data.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The functional role of a cell, shaped by the sophisticated interplay between its molecular identity and spatial context, is often obscured in current spatial modeling. In efforts to model large-scale heterogeneous spatial data in silico effectively and efficiently, we introduce DECIPHER, which disentangles cells' intra-cellular and extra-cellular representation through a novel cross-scale contrast learning strategy. In addition to superior performance over state-of-arts, systematic benchmarks and various real-world case studies showed that the disentangled embeddings produced by DECIPHER enable delineating cell-environment interaction across multiple scales. Of note, DECIPHER is highly scalable, capable of handling spatial atlases with millions of cells which is largely infeasible for existing methods.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.