ArticleGenome biology2025
Joint representation and visualization of derailed cell states with Decipher.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed.
- Spatiotemporal single-cell profiling reveals T cell clonal dynamics and phenotypic plasticity in human graft-versus-host disease.Nature immunology · 2026Article
- LYNX: a deep generative model for linking spatial dynamics and cell interactions in multimodal spatial data.bioRxiv : the preprint server for biology · 2026Article
- SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling.bioRxiv : the preprint server for biology · 2026Article
- Dynamic transitioning between MAPK-driven and WNT-driven cell states drives intestinal cancer and shapes therapy response.Nature genetics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Mapping kinase-dependent tumor immune adaptation with multiplexed single-cell CRISPR screens.bioRxiv : the preprint server for biology · 2026Article
- Joint representation and visualization of derailed cell states with Decipher.Genome biology · 2025Article
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18 authors.
Funding
Abstract
Biological insights often depend on comparing conditions such as disease and health. Yet, we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics. We demonstrate its superior performance across diverse contexts, including in pancreatitis with oncogene mutation, acute myeloid leukemia, and gastric cancer.
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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.