MIT CompBio Lecture 06 - Gene Expression Analysis: Clustering and Classification

MIT Computational Biology: Genomes, Networks, Evolution, Health Prof. Manolis Kellis http://compbio.mit.edu/6.047/ Fall 2018 Lecture 6- Gene expression analysis: Clustering and Classification 1. Introduction to gene expression analysis Technology: microarrays vs. RNAseq. Resulting data matrices Supervised (Clustering) vs. unsupervised (classification) learning 2. K-means clustering (clustering by partitioning) Algorithmic formulation: Update rule, optimality criterion. Fuzzy k-means. Machine learning formulation: Generative models, Expectation Maximization. 3. Hierarchical Clustering (clustering by agglomeration) Basic algorithm, Distance measures. Evaluating clustering results 4. Naïve Bayes classification (generative approach to classification) Discriminant function: class priors, and class-conditional distributions Training and testing, Combine mult features, Classification in practice 5. (optional) Support Vector Machines (discriminative approach) SVM formulation, Margin maximization, Finding the support vectors Non-linear discrimination, Kernel functions, SVMs in practice Slides for Lecture 6: https://stellar.mit.edu/S/course/6/fa...