ArticleProceedings of the National Academy of Sciences of the United States of America2020
Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 42 citations in OpenAlex.
- Article
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- Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE.Cell systems · 2025Article
- Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025Review
- Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.Nucleic acids research · 2025Article
- scStateDynamics: deciphering the drug-responsive tumor cell state dynamics by modeling single-cell level expression changes.Genome biology · 2024Article
- Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation.Cell reports methods · 2024Article
- Stochastic Modeling of Biophysical Responses to Perturbation.bioRxiv : the preprint server for biology · 2024Article
- Learning single-cell perturbation responses using neural optimal transport.Nature methods · 2023Article
- Recovery of missing single-cell RNA-sequencing data with optimized transcriptomic references.Nature methods · 2023Article
- Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data.bioRxiv : the preprint server for biology · 2023Article
- The specious art of single-cell genomics.PLoS computational biology · 2023Article
- Current perspectives on interethnic variability in multiple myeloma: Single cell technology, population pharmacogenetics and molecular signal transduction.Translational oncology · 2022Article
- Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts acrossScience advances · 2021Article
- Reconstructing aspects of human embryogenesis with pluripotent stem cells.Nature communications · 2021Article
- muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data.Nature communications · 2020Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.
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