Speaker: Adam MacLean, University of Southern California https://g.co/kgs/5y6b2f
Since single-cell RNA sequencing technologies have become widespread, great efforts have been made to develop appropriate computational methods to learn biological features from high dimensional datasets. Much less effort has gone into the important yet challenging task of inferring dynamics from these genomic data. Here we present two such efforts. In the first, we capture cell-cell communication at single-cell resolution by incorporating gene regulatory network dynamics and cell-external signaling into a hybrid multiscale model of cell fate decision-making. The results provide surprising insights into cell fate decision-making boundaries during hematopoiesis. In the second, using spatial transcriptomic data linked to dynamic Ca2+ responses in single cells, we use the cell-cell similarity gained from transcriptomics by nonnegative matrix factorization to define informative cell-specific priors. We show that these informative priors dramatically speed up Bayesian parameter inference an ODE model of Ca2+ dynamics in single cells, and analysis of the posterior distributions leads to insight in single-cell variability and individual genes driving phenotypic change.