Zihe Zheng

Pertpy provides a modular framework for analyzing large-scale perturbation single-cell experiments, offering tools for metadata annotation and perturbation distance calculations.

This study optimizes gene knockout in hPSCs using an inducible Cas9 system and identifies effective sgRNAs by comparing experimental results with cleavage efficiency algorithms.

Prophet predicts cellular phenotypes from perturbations using transfer learning, outperforming baselines and enabling in silico screening for therapeutic discovery.

Cell2Sentence converts single-cell gene expression profiles into text-like cell sentences so standard large language models like GPT-2 can be fine-tuned for cell generation and cell-type annotation.