Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. (arXiv:2211.03553v2 [q-bio.GN] UPDATED)

Latent variable models such as the Variational Auto-Encoder (VAE) have become
a go-to tool for analyzing biological data, especially in the field of
single-cell genomics. One remaining challenge is the interpretability of latent
variables as biological processes that define a cell's identity. Outside of
biological applications, this problem is commonly referred to as learning
disentangled representations. Although several disentanglement-promoting
variants of the VAE were introduced, and applied to single-cell genomics data,
this task has been shown to be infeasible from …

arxiv bio cells modeling shift