Analysis of multi-omics data using the Cancer Genomics Cloud
This October, the CGC Webinar Series features a talk by Dr. Min Zhang from Purdue University.
Dr. Min Zhang is a professor in the Department of Statistics and the Associate Director of Data Science at Purdue University Center for Cancer Research. By incorporating biological knowledge in statistical modeling, her research focuses on developing new statistical methods to analyze high-dimensional clinical and omics data that often occurs in biomedical research.
In this presentation, Dr. Zhang will share her experience organizing a workshop on using CGC to access, share, and analyze multi-omics datasets with RNA-seq data as a case study. Dr. Zhang worked with the CGC Seven Bridges team to design a 4 part lecture that taught undergraduate and graduate students how to run a RNA-seq analysis, bulk and single-cell using the CGC. She will also share how she is using the CGC to implement SIGNET for genome-wide gene regulatory network construction.
Example of a SIGNET network
References
Chen C, Zhang D*, Hazbun T*, Zhang M*. (*co-corresponding authors, 2019). Inferring gene regulatory networks from a population of yeast segregants. Scientific Reports. 9(1):1197. <https://www.nature.com/articles/s41598-018-37667-4>
Chen C, Ren M, Zhang M, and Zhang D. (2018). A two-stage penalized least squares method for constructing large systems of structural equations. Journal of Machine Learning Research. 19. <https://www.jmlr.org/papers/volume19/16-225/16-225.pdf>.
More about the speaker
Dr. Min Zhang is a professor in the Department of Statistics and the Associate Director of Data Science at Purdue University Center for Cancer Research. After obtaining an MD with a residency in cancer and a PhD in Neuroscience, she received another PhD from the Department of Biological Statistics and Computational Biology at Cornell University. By incorporating biological knowledge in statistical modeling, she has focused on developing new statistical methods to analyze high-dimensional clinical and omics data that often occurs in biomedical research.