Funda Sar

This study uses longitudinal scRNA-seq to track neuroendocrine transdifferentiation in a prostate cancer model, identifying transcriptional changes and key regulators during treatment-induced NEPC progression.

This study uses longitudinal single-cell RNA sequencing to identify RUNX1T1 as a key regulator in the transition and maintenance of treatment-induced neuroendocrine prostate cancer.

This study identifies rare germline variants associated with metastatic prostate cancer using an extreme phenotype cohort and validates their functional impact.

scMUSCL improves scRNA-seq clustering by transferring knowledge from multiple annotated datasets without requiring batch correction or prior cluster number knowledge.

ScRAT predicts clinical phenotypes from scRNA-seq data using attention-based neural networks, outperforming existing methods with limited samples and identifying critical cell types.

This study develops snpSTARRseq, a high-throughput method to test non-coding variants from GWAS, showing 37% of prostate cancer risk loci alter enhancer activity.