Kalman Reaction Networks
Get the cell differentiation network from a fitted Kalman Reaction Net...
Fit the state-space model to a clonal tracking dataset
Simulate a clonal tracking dataset from a given cell differentiation n...
Get the clone-average of the first two-order smoothing moments from a ...
Get the first two-order smoothing moments from a fitted Kalman Reactio...
Nearest Positive Definite Matrix
This is a stochastic framework that combines biochemical reaction networks with extended Kalman filter and Rauch-Tung-Striebel smoothing. This framework allows to investigate the dynamics of cell differentiation from high-dimensional clonal tracking data subject to measurement noise, false negative errors, and systematically unobserved cell types. Our tool can provide statistical support to biologists in gene therapy clonal tracking studies for a deeper understanding of clonal reconstitution dynamics. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.07.08.499353>.