In this talk, Dr. Qunhua Li will present two statistical methodologies that we recently developed for addressing important challenges in genomics and spatial transcriptomic analyses.

Joint analyses of genomic datasets obtained under various conditions are crucial for understanding tissue-specificity and cell differentiation mechanisms, but they pose computational challenges. To tackle this, I will introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that identifies condition-specific patterns in genomic data. CLIMB enables clustering of genomic features with similar condition-specific patterns and identification of features involved in cell fate commitment. Through the analysis of ChIP-seq, RNA-seq, and DNase-seq datasets, we demonstrate that CLIMB improves statistical precision compared to existing methods while capturing biologically relevant and interpretable clusters.