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Joint representation and visualization of derailed cell states with Decipher [scATAC-seq]

GSE299002 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing 4 samples Submitted 2025/06/05 Platform GPL16791
Summary
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.
Published in
Joint representation and visualization of derailed cell states with Decipher
Nazaret A, Fan JL, Lavallée VP et al. · Genome biology 2025 · PMID 40702544 · doi:10.1186/s13059-025-03682-8
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Also filed as BioProject PRJNA1272091 and SRA study SRP589925. Searching any of these in the dataset finder brings you back here.

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