ArticleNature computational science2024
Interpreting single-cell and spatial omics data using deep neural network training dynamics.
Article in Nature computational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Integrative single-cell and spatial transcriptomics with machine learning identify a Luminal-inflam malignant program and reveal an RPN1-PERK UPR vulnerability in triple-negative breast cancer.Scientific reports · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- Supervised machine learning identifies impaired mitochondrial quality control in β-cells with development of type 2 diabetes.NPJ systems biology and applications · 2026Article
- SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Multi-Channel Neural Interface for Neural Recording and Neuromodulation.Small methods · 2026Review
- Network toxicology and single-cell analysis reveal molecular mechanisms of DEHP-induced colorectal cancer.Bioinformation · 2026Article
- The Application of Omics Technologies in Type II Diabetes Mellitus Research.Current diabetes reviews · 2026Review
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Supervised machine learning identifies impaired mitochondrial quality control in β cells with development of type 2 diabetes.bioRxiv : the preprint server for biology · 2025Article
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- Precision Neuro-Oncology in Glioblastoma: AI-Guided CRISPR Editing and Real-Time Multi-Omics for Genomic Brain Surgery.International journal of molecular sciences · 2025Review
- Identification and Characterization of the Complete Genome of the TGF-International journal of molecular sciences · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Interpreting single-cell and spatial omics data using deep neural network training dynamics.Nature computational science · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell and spatial omics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations or phenotypes. However, cell annotations are inherently ambiguous, as discrete labels with subjective interpretations are assigned to heterogeneous cell populations on the basis of noisy, sparse and high-dimensional data. Here we developed Annotatability, a framework for identifying annotation mismatches and characterizing biological data structure by monitoring the dynamics and difficulty of training a deep neural network over such annotated data. Following this, we developed a signal-aware graph embedding method that enables downstream analysis of biological signals. This embedding captures cellular communities associated with target signals. Using Annotatability, we address key challenges in the interpretation of genomic data, demonstrated over eight single-cell RNA sequencing and spatial omics datasets, including identifying erroneous annotations and intermediate cell states, delineating developmental or disease trajectories, and capturing cellular heterogeneity. These results underscore the broad applicability of annotation-trainability analysis via Annotatability for unraveling cellular diversity and interpreting collective cell behaviors in health and disease.
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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.