Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the temporal relationship between epigenome and transcriptome. To realize this potential, we developed MultiVelo, a differential equation model of gene expression that extends the RNA velocity framework to incorporate epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of chromatin accessibility and gene expression from single-cell data, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Incorporating chromatin accessibility data significantly improves the accuracy of cell fate prediction compared to velocity estimates from RNA only. Fitting MultiVelo on single-cell multi-omic datasets from brain, skin, and blood cells reveals two distinct classes of genes distinguished by whether chromatin closes before or after transcription ceases. Our model also identifies four types of cell states--two states in which epigenome and transcriptome are coupled and two distinct decoupled states. The parameters inferred by MultiVelo quantify the length of time for which genes occupy each of the four states, ranking genes by the degree of coupling between transcriptome and epigenome. Finally, we identify time lags between transcription factor expression and binding site accessibility and between disease-associated SNP accessibility and expression of the linked genes. We provide an open-source Python implementation of MultiVelo on PyPI and GitHub.

Tool Link: https://github.com/welch-lab/MultiVelo

This presentation will be held in 2036 Palmer Commons. There will also be a remote viewing option via Zoom.