SpaOTsc uses optimal transport to 1) project between scRNA-seq data and spatial data, 2) infer spatial cell-cell communications of scRNA-seq data, and 3) identify spatially localized subpopulations.

La Manno et al. used a linear model to relate abundance of pre-mRNA U(t) with abundance of mature mRNA S(t) (La Manno et al., Nature 2018). Given that the molecular regulatory mechanisms between pre-mRNA and mature mRNA are complicated, and in many molecular networks more commonly we observe non-linear (e.g. switch-like) responses, we proposed a nonlinear model of RNA velocity for the effects of pre-mRNA on the abundance of mature mRNA based on Michaelis–Menten kinetics.

Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains.

Hybrid multi-scale computational model that tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination.