ArticleNature computational science2024
Biophysically interpretable inference of cell types from multimodal sequencing data.
Article in Nature computational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions.PLoS computational biology · 2026Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances.bioRxiv : the preprint server for biology · 2025Article
- Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.Nature methods · 2025Article
- Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data.Bioinformatics (Oxford, England) · 2025Article
- Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.Nucleic acids research · 2025Article
- Accurate quantification of nascent and mature RNAs from single-cell and single-nucleus RNA-seq.Nucleic acids research · 2025Article
- Trajectory inference from single-cell genomics data with a process time model.PLoS computational biology · 2025Article
- Stochastic Modeling of Biophysical Responses to Perturbation.bioRxiv : the preprint server for biology · 2024Article
- scCensus: Off-target scRNA-seq reads reveal meaningful biology.bioRxiv : the preprint server for biology · 2024Article
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3 authors.
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Abstract
Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or 'clusters' in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for 'clusters' through the governing parameters of cellular processes.
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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.