ArticleNature communications2018
Discovery of rare cells from voluminous single cell expression data.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed.
- RAGCell: retrieval-augmented generation as supervision for versatile single-cell analysis.Bioinformatics (Oxford, England) · 2026Article
- RareCapsNet: An explainable capsule network enables robust discovery of rare cell populations from large-scale single-cell transcriptomics.PLoS computational biology · 2026Article
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- RAG: a regularized adaptive graph-based method for rare-cell identification from single-cell expression data.Briefings in bioinformatics · 2026Article
- scMagnifier: Resolving fine-grained cell subtypes via GRN-informed perturbations and consensus clustering.PLoS computational biology · 2026Article
- FlashDeconv reveals resolution horizons in atlas-scale spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- scRDiT: Generating Single-cell RNA-seq Data by Diffusion Transformers and Accelerating Sampling.Interdisciplinary sciences, computational life sciences · 2026Article
- Review
- Robust characterization and interpretation of rare pathogenic cell populations from spatial omics using GARDEN.Nature communications · 2026Article
- SLECA: A Single-Cell Atlas of Systemic Lupus Erythematosus Enabling Rare-Cell Discovery Using Graph Transformer.Computational and structural biotechnology journal · 2026Article
- Mast cells act as pro-angiogenic and pro-tumorigenic players in pituitary gonadotroph tumors.Neuro-oncology · 2026Article
- Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.Genome research · 2025Article
- Adaptive resampling for improved machine learning in imbalanced single-cell datasets.bioRxiv : the preprint server for biology · 2025Article
- A novel gene expression stability metric to unveil homeostasis and regulation.Genome biology · 2025Article
- Advancing biological understanding of cellular senescence with computational multiomics.Nature genetics · 2025Review
- Transcript Identification Using Arrayed Hydrogels with TrapFISH.Advanced materials technologies · 2025Article
- Enabling scalable single-cell transcriptomic analysis through distributed computing with Apache spark.Scientific reports · 2025Article
- Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling.Genome biology · 2025Article
- Rapid DNA and RNA isolation from few or single cells using low-cost NAxtra magnetic nanoparticles.Scientific reports · 2025Article
16 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage.
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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.