Factorial latent dynamic models trained on Markovian simulations of biological processes using single cell RNA sequencing data.
PythonGPL-3.0
Factorial latent dynamic models trained on Markovian simulations of biological processes using scRNAseq. data.
With a transition probability matrix $T$ over observed states $O$ and assuming Markovian dynamics,
$P(o \mid i) = P(o \mid o_{i-1})$
For iteration $i$,
$P(o \mid i) = P(o \mid i=0) \cdot T^i$
The animation overlays $P(i \mid o)$ on a 2D UMAP embedding of the data (Cerletti et. al. 2020) Since we are interested in modelling the dynamics in a smaller latent state space, we factorise the MSM simulation,