Inferring differential dynamics from multi-lineage, multi-omic, and multi-sample single-cell data with MultiVeloVAE
Year: 2025
Type: article
Abstract: Understanding how cells differentiate to their final specialized fates is a fundamental problem in biomedical science. Single-cell multi-omic profiling provides an opportunity to identify dynamic molecular changes, but new computational approaches are needed to realize this potential. In particular, previous methods for RNA velocity inference lack support for multi-lineage, multi-sample, and multi-omic single-cell data and cannot be used to identify differential dynamics. To overcome these challenges, we introduce MultiVeloVAE, a probabilistic framework for multi-sample RNA velocity inference that integrates single-cell RNA and multi-omic data. MultiVeloVAE models gene expression and chromatin accessibility on a shared time scale, performs multi-sample inference from datasets with partially overlapping modalities, accounts for lineage bifurcations, and enables statistical testing of velocity parameters among cell types and over time. Using newly generated 10X Multiome datasets from human embryoid bodies and differentiating macrophage cells, we demonstrate that MultiVeloVAE provides novel insights into chromatin accessibility and gene expression dynamics during development. Understanding how cells differentiate to their final fates is a fundamental biological problem. Here, authors introduce MultiVeloVAE, a probabilistic framework that models gene expression and chromatin accessibility mechanistically, integrates multiple samples, accounts for bifurcations, and enables statistical testing over time.
Source: Nature Communications
Institution: University of Michigan
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: Quantitative Definition of Cell Identity by Integrating Transcriptomic, Epigenomic, and Spatial Features of Individual Cells, Single-cell multi-omic analysis of opioid-mediated HIV disease pathogenesis, G1646439160, G3628700528, G7274276017, Integrative Single-Cell Analysis of Transcriptome, Epigenome, and Lineage in HIV Latency and Activation, An Atlas of Human Brain Cell Variation
OpenAlex ID: W4416430925