ArticleNature methods2020
TooManyCells identifies and visualizes relationships of single-cell clades.
Article in Nature methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
54 citing papers in PubMed.
- Natural killer cell-derived extracellular vesicles reprogram cellular human immunity to enhance tumor cytotoxicity.Molecular therapy. Oncology · 2026Article
- Maternal sleep deprivation reshapes offspring hippocampal development through a Trem2-centered microglial program revealed by single-nucleus and spatial transcriptomics.Molecular psychiatry · 2026Article
- Article
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- Single-Cell Sequencing Reveals Circadian Sensitivity of Noise-Induced Hearing Loss Mediated by Macrophage-Driven NLRP3 Inflammasome Activation.Neuroscience bulletin · 2026Article
- 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
- Article
- Single-cell sequencing technology in renal cancer: insights into tumor biology and clinical application.Biomarker research · 2025Review
- Network based simultaneous embedding of cells and marker genes from scRNA-seq studies.Briefings in bioinformatics · 2025Article
- scGGC: a two-stage strategy for single-cell clustering through cellular gene pathway construction.Briefings in bioinformatics · 2025Article
- Exploring the Role of T-Cell Metabolism in Modulating Immunotherapy Efficacy for Non-Small Cell Lung Cancer Based on Clustering.Journal of clinical laboratory analysis · 2025Article
- Single-cell RNA sequencing reveals important role of monocytes and macrophages during mucopolysaccharidosis treatment.Scientific reports · 2025Article
- Cross-Species Insights from Single-Nucleus Sequencing Highlight Aging-Related Hippocampal Features in Tree Shrew.Molecular biology and evolution · 2025Article
- IDclust: Iterative clustering for unsupervised identification of cell types with single cell transcriptomics and epigenomics.NAR genomics and bioinformatics · 2024Article
- Collapsible tree: interactive web app to present collapsible hierarchies.Bioinformatics (Oxford, England) · 2024Article
- Identification of leukemia stem cell subsets with distinct transcriptional, epigenetic and functional properties.Leukemia · 2024Article
- Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis.Clinical and translational medicine · 2024Review
- Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance.Nature communications · 2024Article
- scBubbletree: computational approach for visualization of single cell RNA-seq data.BMC bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering 'resolution' hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.
Indexed as
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.