Dr. Hu’s paper on spatial transcriptomics data analysis won the 2021 ASA Section of Statistics in Genomics and Genetics best student paper award! Dr. Hu will present his work at JSM in August.
Novel Machine Learning for Genomics Data in course Statistical Machine Learning for Data Science This course provides learning opportunities on statistical software, R, with some focus on data management and wrangling, reproducibility, and visualization. On top of that, there are basic introductions to Machine Learning such as k-NN, Naive Bayes, regression methods, etc. The focus […]
This course introduces basic principles of genetic and molecular epidemiology through interactive discussion with leading researchers in the field. This is a stand-alone course but is also a prerequisite for the Genetic and Molecular Epidemiology Certificate Program.
Deciphering tissue microenvironment from Next Generation Sequencing data The advent of high-throughput next-generation sequencing (NGS) technologies has transformed our understanding of cell biology and human disease. As NGS has been adopted earliest by the scientific community, its use has now become widespread, and the technology has improved rapidly. At present, it is now common for […]
SpaGCN is a computational method for data integration to improve the interpretation of spatial expression. SpaGCN focuses on incorporating existing histology to identify spatial domains and subsequently identify genes differentially expressed between the spatial clusters. Link: https://www.nature.com/articles/s41592-021-01272-7