Course info

Monthly user meeting at SciNet with pizza, a techtalk and user discussion: Bayesian Model-Based Clustering Approaches for Discrete-Valued Gene Expression Data by Anjali Silva. Abstract: Unsupervised classification or clustering uses no a priori knowledge of the labels of the observations in the process of categorizing data. This presentation focuses on research surrounding machine learning of discrete-valued gene expression datasets using clustering, with the aim of identifying gene co-expression networks. Specifically, a number of topics surrounding the use of mixture models and Markov chain Monte Carlo (MCMC) methods for clustering of discrete data from high-throughput transcriptome sequencing technologies will be presented. After outlining current challenges and gaps in research with respect to clustering approaches, several mixture model-based clustering methods will be presented. Significance, innovation, limitations and a number of future directions stemming from this research will be discussed.