ArticleCell systems2019
Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape.
Article in Cell systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
- A human single-cell atlas identifies OLR1Nature genetics · 2026Article
- Foundation model reveals the shared organization of transcription and topologically associating domains.Cell systems · 2026Article
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- SKIM: A fast sketching strategy integrated with model's dynamic-feedback for large-scale single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Human body single-cell atlas of three-dimensional genome organization and DNA methylation.Science (New York, N.Y.) · 2026Article
- Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance.Nature methods · 2026Article
- FlashDeconv reveals resolution horizons in atlas-scale spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking sketching methods on spatial transcriptomics data.Nucleic acids research · 2026Article
- An inducible system to study the regulatory functions of GSX2 in human lateral ganglionic eminence-like progenitors.Developmental biology · 2026Article
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- Spatial immune hubs defined by conserved activated dendritic cells are remodeled by immunotherapy.bioRxiv : the preprint server for biology · 2026Article
- Single-cell landscape of lymphoid and myeloid heterogeneity across type-stratified craniofacial lesions in polyostotic fibrous dysplasia.Frontiers in immunology · 2026Article
- Distinct mechanisms of transcriptomic habituation to repeated stress in the mouse hippocampus.Nature communications · 2025Article
- CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data.Nature protocols · 2025Review
- IPGCA: A Comprehensive Single Cell Atlas of 1 074 127 Porcine Intestinal Cells Revealing Cellular Dynamics, Genetic Regulation, and Cross-Species Conservation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Loss of lung mesenchymal identities in culture and a screening approach to identify restorative cues.American journal of physiology. Lung cellular and molecular physiology · 2025Article
- Evaluating the role of pre-training dataset size and diversity on single-cell foundation model performance.bioRxiv : the preprint server for biology · 2025Article
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- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- Integrative analysis reveals generalizable human neurodegenerative disease-associated glial states.bioRxiv : the preprint server for biology · 2025Article
40 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Large-scale single-cell RNA sequencing (scRNA-seq) studies that profile hundreds of thousands of cells are becoming increasingly common, overwhelming existing analysis pipelines. Here, we describe how to enhance and accelerate single-cell data analysis by summarizing the transcriptomic heterogeneity within a dataset using a small subset of cells, which we refer to as a geometric sketch. Our sketches provide more comprehensive visualization of transcriptional diversity, capture rare cell types with high sensitivity, and reveal biological cell types via clustering. Our sketch of umbilical cord blood cells uncovers a rare subpopulation of inflammatory macrophages, which we experimentally validated. The construction of our sketches is extremely fast, which enabled us to accelerate other crucial resource-intensive tasks, such as scRNA-seq data integration, while maintaining accuracy. We anticipate our algorithm will become an increasingly essential step when sharing and analyzing the rapidly growing volume of scRNA-seq data and help enable the democratization of single-cell omics.
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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.