ArticleNature methods2023
Population-level integration of single-cell datasets enables multi-scale analysis across samples.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed.
- ArchMap is a web-based platform for reference-based analysis of single-cell datasets.Nature genetics · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep learning representations of human Immune Health for precision immunology.bioRxiv : the preprint server for biology · 2026Article
- PromptSTG: prototype-guided prompting for few-shot spatial transcriptomics annotation.Briefings in bioinformatics · 2026Article
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- Hierarchical classification of immune cell transcriptomes at population-scale.bioRxiv : the preprint server for biology · 2026Article
- Mapping the transcriptional diversity of calcium signaling in the mouse and human brain.iScience · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- Transient YAP activation uncovers the neurogenic potential of proliferative mammalian Müller glia.PNAS nexus · 2026Article
- Trypanosoma cruzi cell atlas as a single-cell resource for understanding parasite population heterogeneity and differentiation.Nature communications · 2026Article
- Robust integration of single-cell datasets with imbalanced modality composition.Nature communications · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- scMILD: Single-cell multiple instance learning for sample classification and associated subpopulation discovery.iScience · 2026Article
- Limitations of cell embedding metrics assessed using drifting islands.Nature biotechnology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- Benchmarking LLM-based agents for single-cell omics analysis.Genome biology · 2026Article
- A Pan-Cancer Single-Cell Atlas to Evaluate Tumor Identity, Cell Line Concordance, and Dependency Mapping.bioRxiv : the preprint server for biology · 2026Article
- scSHEFT enables multiomics label transfer from scRNA-seq to scATAC-seq through dual alignment.Genome research · 2026Article
- Interpretable inflammation landscape of circulating immune cells.Nature medicine · 2026Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.
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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.