ArticleNature communications2020
A multiresolution framework to characterize single-cell state landscapes.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single-Cell RNA-seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Coordinated neural progenitor adaptations contribute to primate neocortical downscaling.Science advances · 2026Article
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- Epigenetically constrained astrocyte states underlie prefrontal cortex vulnerability in Down syndrome-associated Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- Voltage-gated ion channel diversity underlies neuronal excitability and nervous system evolution.Nature communications · 2025Article
- Machine learning predicts distinct biotypes of amyotrophic lateral sclerosis.European journal of human genetics : EJHG · 2025Article
- Pan-cancer drivers of metastasis.Molecular cancer · 2025Article
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
- Multisensory gamma stimulation enhances adult neurogenesis and improves cognitive function in male mice with Down Syndrome.PloS one · 2025Article
- Mapping Cell Identity from scRNA-seq: A primer on computational methods.Computational and structural biotechnology journal · 2025Review
- Article
- mosaicMPI: a framework for modular data integration across cohorts and -omics modalities.Nucleic acids research · 2024Article
- Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.bioRxiv : the preprint server for biology · 2024Article
- Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease.Aging and disease · 2024Article
- APOE2 protects against Aβ pathology by improving neuronal mitochondrial function through ERRα signaling.Cellular & molecular biology letters · 2024Article
- Single-cell multi-cohort dissection of the schizophrenia transcriptome.Science (New York, N.Y.) · 2024Article
- scLENS: data-driven signal detection for unbiased scRNA-seq data analysis.Nature communications · 2024Article
- Article
- Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet's superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex.
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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.