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Title: New Advances in High-Dimensional DNA Methylation Analysis in Cancer Epigenetic Using Trans-dimensional Hidden Markov Models

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Abstract:

Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this talk, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

Speaker

Farhad Shokoohi is currently serving as an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV) since 2019. He obtained his Ph.D. in Statistics from Shahid Beheshti University in Iran in 2012. Following his doctoral studies, Farhad completed three postdoctoral fellowships at the University of Manitoba (2013) and 91社区 (2013-2014, 2014-2016). Subsequently, he held the position of Assistant Professor at Concordia University from 2017 to 2018. Farhad has also held visiting scholar positions at the University of Perugia, Italy, and Ohio State University, USA.

Farhad鈥檚 research expertise spans diverse areas of statistical analysis, including high-dimensional data analysis, Bayesian and frequentist methods, machine learning, statistical genomics and genetics, computational statistics, and software development, among other fields.

Meeting ID: 830 0817 4313

Passcode: None

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