Hao Wu, PhD
Department of Biostatistics and Bioinformatics
Rollins School of Public Health
ABSTRACT: Tissue samples obtained from clinical practices are usually mixtures of different cell types. The “bulk” high-throughput omics data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis and will lead to biased results if not properly accounted for. In this talk, I will present our recent method work for analyzing high-throughput data from heterogeneous samples, including estimating and account for tumor purity in cancer genomics data, cell type-specific differential analysis, and reference-free signal deconvolution. I will also share some experiences and thoughts on the data analysis strategy and potential future research direction in this field.
The meeting agenda includes a presentation, programmatic announcements, and updates from Cancer Center program leaders.
This group meets once a month from noon – 1 p.m.