Po‐Yuan Tung

SO_SCPLOWTACKC_SCPLOW is a foundation model trained on human single-cell data that enables in-context learning for predicting perturbation effects without fine-tuning.

ProPer-seq is a cost-effective, barcode-free single-cell perturbation profiling method that enables scalable analysis of synthetic biology perturbations and reveals transcriptional control principles.

The paper develops a method to quantify cell cycle progression in scRNA-seq data using a small number of genes, showing it can predict cell cycle phase within 14% accuracy.

This study identifies variance QTLs (vQTLs) in human iPSCs using scRNA-seq, finding that genetic variants affect gene expression variance but with smaller phenotypic impact than mean expression QTLs.

The paper examines regulatory variation in iPSCs and differentiated cells, finding lower individual variation in iPSCs and identifying shared chromatin regions for some cell type-specific QTLs.