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Free Course: Network Analysis

  • 01 Oct 2013
  • Online
https://www.coursera.org/course/netsysbio

Network Analysis in Systems Biology

Avi Ma’ayan, PhD

An introduction to network analysis and statistical methods used in contemporary Systems Biology and Systems Pharmacology research.

Workload: 6-8 hours/week 
Watch intro video

Sessions:
October 2013 (7 weeks long) Sign Up
Future sessions
 

About the Course

The course Network Analysis in Systems Biology provides an introduction to network analysis and statistical methods used in contemporary Systems Biology and Systems Pharmacology research. Students will learn how to construct, analyze and visualize different types of molecular networks, including gene regulatory networks connecting transcription factors to their target genes, protein-protein interaction networks, cell signaling pathways and networks, metabolic networks, drug-target and drug-drug similarity networks and functional association networks. Methods to process raw data from genome-wide RNA (microarrays and RNA-seq) and proteomics (IP-MS and phosphoproteomics) profiling will be presented. Processed data will be clustered, and gene-set enrichment analyses methods will be covered. The course will also discuss topics in network systems pharmacology including processing and using databases of drug-target interactions, drug structure, drug/adverse-events, and drug induced gene expression signatures.  

We take a case-based approach to teach contemporary statistical and network analysis methods used to analyze data within Systems Biology and Systems Pharmacology research. The course is appropriate for beginning graduate students and advanced undergraduates. Lectures provide background knowledge in understanding the properties of large datasets collected from mammalian cells. In the course we will teach how these datasets can be analyzed to extract new knowledge about the system. Such analyses include clustering, data visualization techniques, network construction and visualization, and gene-set enrichment analyses. The course will be useful for students who encounter large datasets in their own research, typically genome-wide and would like to learn different methods to understand such data. The course will teach the students how to use existing software tools such as those developed by the Ma’ayan Laboratory at Mount Sinai, but also other freely available software tools. In addition the course requires the students to write short scripts in Python. Students can bring their own data to the course and utilize the methods they learn to analyze their own data for their own projects.

Course Syllabus

Topics covered include:
  • Types of Biological Networks
  • Principles of Graph Theory and Set Theory for Network Analysis
  • Clustering Algorithms
  • Principal Component Analysis and Singlular Value Decomposition
  • Multivariable, Logistic, and Partial Least Squares Regression
  • Gene Set Enrichment Analysis
  • Gene Expression Data Analysis: Microarrays and RNA-seq
  • Genomic Analysis: CNVs, ChIP-seq and DNA Methylation
  • Analysis of Proteomics and Phosphoproteomics Datasets
  • Integrating Multiple Types of Large Datasets
  • Machine Learning Techniques in Systems Biology
  • Lists2Networks and Enrichr: Gene Set Libraries and Enrichment Analysis
  • Building Networks: Network Expansion and Utilizing Prior Knowledge for Hypothesis Generation
  • Visualization of Networks
  • Analysis of Network Topology I
  • Analysis of Network Topology II
  • From Gene Expression Signatures to Cell Signaling: ChEA, Genes2Networks, KEA and Expression2Kinases
  • Network Pharmacology: Drug-Drug Similarity and Drug-Target Networks
  • Methods to Analyze Network Dynamics using Boolean Networks

Recommended Background

Basic courses in statistics and molecular biology are required. Courses in bioinformatics, computer science, and physics, are not required but preferred. Ability to write short scripts in languages such as Python would be useful. 

Suggested Readings

Review articles and selected original research articles will be discussed in the lectures and can enhance understanding, but these are not required to complete the course. All materials will be from open access journals or will be provided as links to e-reprints, so there will be no cost to the student.

Course Format

The class will consist of lecture videos, which are between 8 and 15 minutes in length.  Each lecture will include sample problems to enable the student to practice the methods discussed.

For evaluation, students will be given assignments for each lecture that will typically require the analysis of large datasets or building and analysis of models. Illustrative examples from the published literature will be used. The assignments will also contain multiple choice questions that test the biological understanding of the computational model or summarize the results from various analyses.



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