ArticleNature computational science2021
Single-cell manifold-preserving feature selection for detecting rare cell populations.
Article in Nature computational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 59 citations in OpenAlex.
- Donor-specific, but not sex-specific, signatures dominate cell-type classification in lung scRNA-seq data.Biochemistry and biophysics reports · 2026Article
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- RECOMBINE identifies recurrent composite markers of cell types and states.Genome research · 2026Article
- CiCLoDS: Joint cell clustering and gene selection for single-cell spatial transcriptomics.Scientific reports · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.Nature communications · 2026Article
- CellScope: high-performance cell atlas workflow with tree-structured representation.Nature communications · 2025Article
- Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.Genome research · 2025Article
- A novel gene expression stability metric to unveil homeostasis and regulation.Genome biology · 2025Article
- CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold.Nature cell biology · 2025Article
- Multi-view gene panel characterization for spatially resolved omics.Briefings in bioinformatics · 2025Article
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.Communications biology · 2025Article
- Article
- Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.Nature reviews. Molecular cell biology · 2025Review
- Homebuilt Imaging-Based Spatial Transcriptomics: Tertiary Lymphoid Structures as a Case Example.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Probe set selection for targeted spatial transcriptomics.Nature methods · 2024Article
- DELVE: feature selection for preserving biological trajectories in single-cell data.Nature communications · 2024Article
- MarkerMap: nonlinear marker selection for single-cell studies.NPJ systems biology and applications · 2024Article
- Gene panel selection for targeted spatial transcriptomics.Genome biology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
A key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations (RCPs) that drive development, differentiation, and transformation. Molecular features such as genes and proteins defining RCPs are often unknown and difficult to detect from unenriched single-cell data, using conventional dimensionality reduction and clustering-based approaches. Here, we propose an unsupervised approach, SCMER (Single-Cell Manifold presERving feature selection), which selects a compact set of molecular features with definitive meanings that preserve the manifold of the data. We applied SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis, and drug resistance and response. We found that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states. SCMER can be used for discovering molecular features in a high dimensional dataset, designing targeted, cost-effective assays for clinical applications, and facilitating multi-modality integration.
Identifiers
What OpenQuestion holds
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.