ArticleeLife2019
Self-assembling manifolds in single-cell RNA sequencing data.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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Who cites it
50 citing papers in PubMed.
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- Comparative single-cell atlases reveal injury-driven tubal epithelial regeneration as a window for ovarian carcinoma initiation.bioRxiv : the preprint server for biology · 2026Article
- Schistosome esophageal gland factor MEG-8.2 drives host cell lysis and interacts with host immune proteins.PLoS pathogens · 2026Article
- Spatial transcriptomics of developing human lungs defines cellular phenotypes associated with age, lineage and location.Scientific reports · 2026Article
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- Explosive cytotoxicity of 'ruptoblasts' bridges hormonal surveillance and immune defense.bioRxiv : the preprint server for biology · 2025Article
- Bioengineering a Patient-Derived Vascularized Lung Tumor-on-Chip Model to Decipher Immunomodulation by the Endothelium.Advanced healthcare materials · 2025Article
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- Genotype-integrated single-cell transcriptome analysis reveals the role of DDX41 pR525H in a patient with myelodysplastic neoplasms.Scientific reports · 2025Article
- In vivo self-renewal and expansion of quiescent stem cells from a non-human primate.Nature communications · 2025Article
- Comparative transcriptomic insights into the evolution of vertebrate photoreceptor types.Current biology : CB · 2025Article
- Regeneration in the absence of canonical neoblasts in an early branching flatworm.Nature communications · 2025Article
- Single-cell and bulk RNA sequencing analysis reveals CENPA as a potential biomarker and therapeutic target in cancers.PloS one · 2025Article
- Single-cell Technology in Stem Cell Research.Current stem cell research & therapy · 2025Review
- Implementation and validation of single-cell genomics experiments in neuroscience.Nature neuroscience · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of
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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.