arcinstitute/ST-SE-Parse

State Transition (ST) models using X_state inputs on Replogle. This repository contains 4 fewshot and 4 zeroshot canonical models, with their saved configurations, loading files and available CellEval v1 outputs.

!PIVOT overview

A diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.

Distributional flow matching model for robust single-cell perturbation prediction, modeling the full distribution of perturbed cellular expression profiles conditioned on control states via PAD-Transformer and multi-kernel MMD regularization; reduces MSE by 19.6% over the strongest baseline in combinatorial settings (Westlake University, 41+ stars, MIT License)

CondSCVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space. The predictions of the model are meant to be afterward used for deconvolution of a second spatial transcriptomics dataset in DestVI.