This course is part of the Systems Biology and Biotechnology Specialization

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187 ratings

Avi Ma’ayan, PhD

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Systems Biology and Biotechnology SpecializationIcahn School of Medicine at Mount Sinai

About this Course

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An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology.

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Course 3 of 6 in the

Systems Biology and Biotechnology Specialization

Intermediate Level

Approx. 30 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 3 of 6 in the

Systems Biology and Biotechnology Specialization

Intermediate Level

Approx. 30 hours to complete

English

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Try Coursera for Business

Instructor

Instructor rating

4.33/5 (15 Ratings)

Avi Ma’ayan, PhD

Director, Mount Sinai Center for Bioinformatics

Professor, Department of Pharmacological Sciences

23,516 Learners

2 Courses

Offered by

Icahn School of Medicine at Mount Sinai

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Syllabus - What you will learn from this course

Content Rating92%(1,359 ratings)

Week

Week 1

3 hours to complete

Course Overview and Introductions

The 'Introduction to Complex Systems' module discusses complex systems and leads to the idea that a cell can be considered a complex system or a complex agent living in a complex environment just like us. The 'Introduction to Biology for Engineers' module provides an introduction to some central topics in cell and molecular biology for those who do not have the background in the field. This is not a comprehensive coverage of cell and molecular biology. The goal is to provide an entry point to motivate those who are interested in this field, coming from other disciplines, to begin studying biology.

3 hours to complete

3 videos (Total 52 min), 4 readings, 3 quizzes

Week

Week 2

3 hours to complete

Topological and Network Evolution Models

3 hours to complete

4 videos (Total 45 min)

Week

Week 3

3 hours to complete

Types of Biological Networks

3 hours to complete

4 videos (Total 58 min)

4 videos

Types of Biological Networks12m

Genes2Networks and Network Visualization16m

Sets2Networks - Creating Functional Association Networks14m

Genes2FANs - Analyzing Gene Lists with Functional Association Networks14m

4 practice exercises

Types of Biological Networks30m

Genes2Networks and Network Visualization30m

Functional Association Networks with Sets2Networks30m

Functional Association Networks with Genes2FANs30m

Week

Week 4

2 hours to complete

Data Processing and Identifying Differentially Expressed Genes

2 hours to complete

5 videos (Total 41 min)

Week

Week 5

6 hours to complete

Gene Set Enrichment and Network Analyses

In the 'Gene Set Enrichment and Network Analyses' module the emphasis is on tools developed by the Ma'ayan Laboratory to analyze gene sets. Several tools will be discussed including: Enrichr, GEO2Enrichr, Expression2Kinases and DrugPairSeeker. In addition, one lecture will be devoted to a method we call enrichment vector clustering we developed, and two lectures will describe the popular gene set enrichment analysis (GSEA) method and an improved method we developed called principal angle enrichment analysis (PAEA).

6 hours to complete

9 videos (Total 139 min), 1 reading, 8 quizzes

9 videos

Enrichment Analysis and Enrichr21m

GEO2Enrichr: A Google Chrome Extension for Gene Set Extraction and Enrichment7m

Gene Set Enrichment Analysis (GSEA) - Preliminaries13m

Gene Set Enrichment Analysis (GSEA) - Part 28m

Principal Angle Enrichment Analysis (PAEA)18m

Network2Canvas (N2C) and Enrichment Analysis with N2C17m

Expression2Kinases: Inferring Pathways from Differentially Expressed Genes24m

DrugPairSeeker and the New CMAP17m

Classifying Patients/Tumors from TCGA11m

1 reading

GATE Desktop Software Tool10m

8 practice exercises

The Fisher Exact Test and Enrichr30m

Gene Set Enrichment Analysis (GSEA) - Part 130m

Gene Set Enrichment Analysis (GSEA) - Part 230m

Principal Angle Enrichment Analysis (PAEA)30m

GATE and Network2Canvas30m

Expression2Kinases30m

DrugPairSeeker and the New CMAP30m

Classifying Patients from TCGA30m

Week

Week 6

6 hours to complete

Deep Sequencing Data Processing and Analysis

A set of lectures in the 'Deep Sequencing Data Processing and Analysis' module will cover the basic steps and popular pipelines to analyze RNA-seq and ChIP-seq data going from the raw data to gene lists to figures. These lectures also cover UNIX/Linux commands and some programming elements of R, a popular freely available statistical software. Note that since these lectures were developed and recorded during the Fall of 2013, it is possible that there are better tools that should be used now since the field is rapidly advancing.

6 hours to complete

7 videos (Total 125 min)

7 videos

RNA-seq Analysis - Preliminaries18m

RNA-seq Analysis - Using TopHat and Cufflinks21m

RNA-seq Analysis - R Basics23m

RNA-seq Analysis - CummeRbund23m

STAR: An Ultra-fast RNA-seq Aligner13m

ChIP-seq Analysis - Part 113m

ChIP-seq Analysis - Part 212m

7 practice exercises

RNA-seq and UNIX/Linux Commands30m

RNA-seq Pipeline30m

CummeRbund and R Programming30m

CummeRbund - Demo30m

RNA-seq STAR30m

ChIP-seq Analysis - Part 130m

ChIP-seq Analysis - Part 230m

Week

Week 7

5 hours to complete

Principal Component Analysis, Self-Organizing Maps, Network-Based Clustering and Hierarchical Clustering

5 hours to complete

6 videos (Total 90 min), 1 reading, 6 quizzes

6 videos

Principal Component Analysis (PCA) - Part 113m

Principal Component Analysis (PCA) - Part 28m

Principal Component Analyis (PCA) Plotting in MATLAB15m

Clustergram in MATLAB14m

Self-Organizing Maps14m

Network-Based Clustering24m

1 reading

MATLAB License10m

6 practice exercises

Principal Component Analysis (PCA) - Part 130m

Principal Component Analysis (PCA) - Part 230m

Principal Component Analysis (PCA) with MATLAB30m

Hierarchical Clustering (HC) with MATLAB30m

Self-Organizing Maps30m

Network-Based Clustering30m

Week

Week 8

2 hours to complete

Resources for Data Integration

The lectures in the 'Resources for Data Integration' module are about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. These lectures start with the idea of functional association networks (FANs). Following this lecture are several lectures that discuss how to construct FANs from various resources and how to use these networks for analyzing gene lists as well as to construct a puzzle that can be used to connect genomic data with phenotypic data.

2 hours to complete

5 videos (Total 49 min)

Week

Week 9

1 hour to complete

Crowdsourcing: Microtasks and Megatasks

The final set of lectures presents the idea of crowdsourcing. MOOCs provide the opportunity to work together on projects that are difficult to complete alone (microtasks) or compete for implementing the best algorithms to solve hard problems (megatasks). You will have the opportunity to participate in various crowdsourcing projects: microtasks and megatasks. These projects are designed specifically for this course.

1 hour to complete

2 videos (Total 19 min)

Week

Week 10

1 hour to complete

Final Exam

1 hour to complete

Reviews

4.5

29 reviews

5 stars
64.36%
4 stars
23.93%
3 stars
9.04%
2 stars
0.53%
1 star
2.12%

TOP REVIEWS FROM NETWORK ANALYSIS IN SYSTEMS BIOLOGY

by AFDec 16, 2018

Exciting course. I think some contents should be updated but in general an exhaustive overview.

by FPJun 2, 2016

Excellent course to get deep into the data analysis of system biology experimentation.

by PRJul 21, 2020

Various analytical approaches for network analysis are very well explained. Also, have explained the working of different bioinformatics or network-based tools and software.

by DBApr 27, 2019

It was a good review of various tools, but maybe it was to many tools. I think it would be nice to show a smaller number of tools, but make more reproducible showcases

View all reviews

About the Systems Biology and Biotechnology Specialization

Design systems-level experiments using appropriate cutting edge techniques, collect big data, and analyze and interpret small and big data sets quantitatively.

The Systems Biology Specialization covers the concepts and methodologies used in systems-level analysis of biomedical systems. Successful participants will learn how to use experimental, computational and mathematical methods in systems biology and how to design practical systems-level frameworks to address questions in a variety of biomedical fields. In the final Capstone Project, students will apply the methods they learned in five courses of specialization to work on a research project.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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