Presented by: Qunhua Li, PhD, Associate Professor, Dept of Statistics, Associate Chair, Bioinformatics & Genomics Graduate Program, Penn State

About the presentation: Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this, I will present CLIMB (Composite Likelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.

This is a hybrid event. Individuals can attend this seminar in person in ASB, Room 2200G, via phone or via Zoom.

To attend via the phone, dial 929-205-6099

To attend via Zoom, visit https://pshealth.zoom.us/j/94860982030

The meeting ID is 948 6098 2030 and the passcode is 320367