Table of Contents18.417 Lecture 14 Announcements Regulatory Networks: Example Regulatory Networks: Approaches Regulatory Networks: Goal Expression Clustering The problem Expression Profiles Clustering expression levels 1. Expression Value Normalization 2. Feature extraction 3. Clustering Algorithms 3a. Hierarchical clustering 3b. K-means clustering 3c. Cluster Representation 3d. Cluster distance metrics PPT Slide Other clustering methods Evaluating clustering output Visualizing clustering output Rearranging tree branches What have we learned? Location Analysis The question Footprint experiments Chromatin IP (ImmunoPrecipitation) Location Analysis What have we learned? Sequence Conservation I same gene across species The idea PPT Slide Applying Bayes’ Law Motif divergence and evolutionary time PPT Slide PPT Slide Motif degeneracy and Random Occurrence Signal to Noise in Multiple Species PPT Slide Sequence conservation II across co-regulated genes The idea The Algorithms Suffix Trees (review) PPT Slide Gibbs sampling ideas EM: Expectation Maximization Implementation From Clusters to Motifs Sequence Conservation Bayes Networks Modeling the dependencies Bayesian network topology PPT Slide Evaluating Alternative Hypotheses PPT Slide Model Comparison Scoring all possible models Summary Regulatory Networks: The methods |
Author: Manolis Kamvysselis
Email: manoli@mit.edu Home Page: http://theory.lcs.mit.edu/~bab/01-18.417-home.html Other information: |