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Avi Ma’ayan, PhD
This course introduces data analysis methods used in 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, clustering, dimensionality reduction, differential expression, enrichment analysis, and network construction. The course contains practical tutorials for using several bioinformatics tools and setting up data analysis pipelines, also covering the mathematics behind the methods applied by these tools and workflows. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, statistics, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for wet- and dry-lab 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 in New York City, but also other freely available data analysis and visualization tools. The overarching goal of the course is to enable students to utilize the methods presented in this course for analyzing their own data for their own projects. For those students that do not work in the field, the course introduces research challenges faced in the fields of computational systems biology and systems pharmacology.
Register for this course and see more details by visiting:
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What's inside
Syllabus
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.
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Syllabus
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.
Read more
Topological and Network Evolution Models
In the 'Topological and Network Evolution Models' module, we provide several lectures about a historical perspective of network analysis in systems biology. The focus is on in-silico network evolution models. These are simple computational models that, based of few rules, can create networks that have a similar topology to the molecular networks observed in biological systems.
Types of Biological Networks
The 'Types of Biological Networks' module is about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. This lecture ends with the idea of functional association networks (FANs). Following this lecture are lectures that discuss how to construct FANs and how to use these networks for analyzing gene lists.
Data Processing and Identifying Differentially Expressed Genes
This set of lectures in the 'Data Processing and Identifying Differentially Expressed Genes' module first discusses data normalization methods, and then several lectures are devoted to explaining the problem of identifying differentially expressed genes with the focus on understanding the inner workings of a new method developed by the Ma'ayan Laboratory called the Characteristic Direction.
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).
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.
Principal Component Analysis, Self-Organizing Maps, Network-Based Clustering and Hierarchical Clustering
This module is devoted to various method of clustering: principal component analysis, self-organizing maps, network-based clustering and hierarchical clustering. The theory behind these methods of analysis are covered in detail, and this is followed by some practical demonstration of the methods for applications using R and MATLAB.
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.
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.
Final Exam
The final exam consists of multiple choice questions from topics covered in all of modules of the course. Some of the questions may require you to perform some of the analysis methods you learned throughout the course on new datasets.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines data analysis methods commonly used in systems biology, bioinformatics, and systems pharmacology disciplines
Uses real-world datasets and practical exercises to solidify understanding of data analysis methods
Introduces mathematical concepts underpinning data analysis methods and workflows
Emphasizes the importance of data analysis in modern biological and medical research
Incorporates tools developed by the Ma'ayan Laboratory at Mount Sinai for data analysis and visualization
May require additional software or resources not readily available to all learners
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Reviews summary
Informative network analysis course
According to students, Network Analysis in Systems Biology is an informative course that covers a wide range of topics related to bioinformatics. Students largely appreciate the course's well-organized content and extensive use of reference materials. However, some students express frustration with the challenging assessments and find that the course's focus on specific software and tools limits its generalizability. Additionally, a few students note that some sections of the course may be outdated or difficult to understand without prerequisite knowledge.
Course covers a broad range of subjects.
"...covers a wide scope of topics in a (relatively) short amount of time..."
Content is presented in a logical manner.
"It packs a lot of info, but is very well organized and consistent, a big improvement from the other courses."
Valuable information provided.
"Very informative course."
"I have learned alot from this course."
"My sincere thanks to Dr. Avi Ma'ayan, students and researchers for preparing this course."
Course content is not up-to-date.
"The course is more than 10 years old, so many of the tools introduced are not really standard anymore."
Exams or assignments are difficult.
"really good course but the final assessment was gruelling"
"A lot of useful information but the tests are very annoying."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Network Analysis in Systems Biology with these
activities:
Develop a functional association network
Show steps
Reinforce your knowledge by creating a functional association network based on the methods discussed in the lectures.
Show steps
-
Gather gene expression data
-
Select a gene set of interest
-
Construct a network using your chosen method
-
Analyze the network
Explore Enrichr
Show steps
Following this lecture, you can explore the Enrichr website and its tutorials to gain a deeper understanding of gene set enrichment analysis.
Show steps
-
Visit the Enrichr website
-
Review the tutorials
-
Complete the practice exercises
Discuss network-based clustering methods
Show steps
Engage in a peer discussion to explore different network-based clustering methods and their advantages and disadvantages, building on the concepts learned in the lecture.
Show steps
-
Form study groups
-
Assign each group a different network-based clustering method
-
Have each group present their assigned method
-
Facilitate a discussion comparing and contrasting the methods
Two other activities
Expand to see all activities and additional details
Show all five activities
Identify differentially expressed genes
Show steps
To enhance your understanding of data normalization and identifying differentially expressed genes, complete practice problems based on the lectures.
Browse courses on
Data Normalization
Show steps
-
Download the practice dataset
-
Normalize the data
-
Identify differentially expressed genes
Explain principal component analysis
Show steps
Test your understanding by creating a tutorial on the theory and application of principal component analysis, covering the topics discussed in the lecture.
Browse courses on
Principal Component Analysis
Show steps
-
Write a brief introduction to PCA
-
Explain the mathematical basis of PCA
-
Describe the applications of PCA
-
Provide an example of PCA using R or Matlab
Develop a functional association network
Show steps
Reinforce your knowledge by creating a functional association network based on the methods discussed in the lectures.
Show steps
Show steps
Show steps
- Gather gene expression data
- Select a gene set of interest
- Construct a network using your chosen method
- Analyze the network
Explore Enrichr
Show steps
Following this lecture, you can explore the Enrichr website and its tutorials to gain a deeper understanding of gene set enrichment analysis.
Show steps
Show steps
Show steps
- Visit the Enrichr website
- Review the tutorials
- Complete the practice exercises
Discuss network-based clustering methods
Show steps
Engage in a peer discussion to explore different network-based clustering methods and their advantages and disadvantages, building on the concepts learned in the lecture.
Show steps
Show steps
Show steps
- Form study groups
- Assign each group a different network-based clustering method
- Have each group present their assigned method
- Facilitate a discussion comparing and contrasting the methods
Two other activities
Expand to see all activities and additional details
Show all five activities
Identify differentially expressed genes
Show steps
To enhance your understanding of data normalization and identifying differentially expressed genes, complete practice problems based on the lectures.
Browse courses on
Data Normalization
Show steps
Show steps
Show steps
- Download the practice dataset
- Normalize the data
- Identify differentially expressed genes
Explain principal component analysis
Show steps
Test your understanding by creating a tutorial on the theory and application of principal component analysis, covering the topics discussed in the lecture.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Write a brief introduction to PCA
- Explain the mathematical basis of PCA
- Describe the applications of PCA
- Provide an example of PCA using R or Matlab
Career center
Learners who complete Network Analysis in Systems Biology will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist, particularly in the fields of biomedical research, pharmaceutical development, and precision medicine, who has earned this course's certificate will be well-prepared to analyze large and complex biological datasets. The course will provide a solid foundation in data analysis, dimensionality reduction, and gene set enrichment analysis.
Computational Biologist
Computational Biologist
A Computational Biologist who has earned this course's certificate will extend an analytic foundation in the construction and analysis of biological networks for drug discovery and personalized medicine research.
Research Scientist
Research Scientist
A Research Scientist, particularly in the fields of systems biology, computational biology, and precision medicine, who has earned this course's certificate will be well-prepared to analyze large and complex biological datasets. The course will provide a solid foundation in data analysis, dimensionality reduction, and gene set enrichment analysis.
Postdoctoral Researcher
Postdoctoral Researcher
A Postdoctoral Researcher who has earned this course's certificate can leverage skills in data analysis, dimensionality reduction, and statistical modeling to design and conduct research projects in the field of computational biology.
Associate Research Scientist
Associate Research Scientist
An Associate Research Scientist in a pharmaceutical company or biotechnology organization who has earned this course's certificate is better prepared to analyze biological systems to discover new drug targets, biomarkers and side effects. Taking the course will help build a strong foundation in data analysis, dimensionality reduction, and gene set enrichment analysis, all of which are essential for identifying and characterizing drug targets.
Bioinformatics Scientist
Bioinformatics Scientist
A Bioinformatics Scientist who has earned this course's certificate is better equipped for the analytical challenges of working with large datasets, including processing, analyzing, and visualizing data. The course will provide a solid understanding of Python for data analysis as well as Statistical software (R).
Pharmaceutical Scientist
Pharmaceutical Scientist
A Pharmaceutical Scientist who has earned this course's certificate will be better prepared for the analytical challenges of working with large datasets, including processing, analyzing, and visualizing data. The course will provide a solid understanding of Python for data analysis as well as Statistical software (R).
Data Analyst
Data Analyst
A Data Analyst who has earned this course's certificate can leverage skills in data processing, dimensionality reduction, and statistical modeling to identify, extract, and interpret meaningful patterns and trends from complex datasets in the healthcare industry.
Science Writer
Science Writer
A Science Writer specializing in biotechnology or medicine who has earned this course's certificate will be better equipped to understand and write about the latest advances in the field. The course will provide a solid foundation in the principles of systems biology, network analysis, and data analysis.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer who has earned this course's certificate can leverage skills in data analysis, dimensionality reduction, and supervised learning to develop and deploy machine learning models for various applications in the healthcare industry.
Software Engineer
Software Engineer
A Software Engineer who has earned this course's certificate can leverage skills in data analysis, dimensionality reduction, and statistical modeling to develop and implement software solutions for the healthcare industry.
Geneticist
Geneticist
A Geneticist who has earned this course's certificate can build upon the foundational knowledge learned in the classroom regarding gene regulation, gene expression, genetic variation, and genetic diseases to identify genetic risk factors for disease.
Research Analyst
Research Analyst
A Research Analyst who has earned this course's certificate can build upon knowledge previously learned in research methods, biostatistics, and epidemiology to study the causes and prevalence of disease in populations.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst in the healthcare or biotechnology industry who has earned this course's certificate can leverage skills in data analysis, statistical modeling, and financial modeling to develop and implement trading strategies.
Medical Physicist
Medical Physicist
A Medical Physicist who has earned this course's certificate may apply the principles of data analysis, dimensionality reduction, and statistical modeling to interpret medical images and develop computational models for treatment planning and patient care.
For more career information including salaries, visit:
OpenCourser.com/course/g8f8zs/network
Reading list
We've selected 20 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Network Analysis in Systems Biology.
Save
Provides a comprehensive overview of systems biology, covering topics such as network analysis, genetic regulatory networks, and metabolic pathways. It useful reference for students and researchers in the field.
Provides a comprehensive introduction to biological networks, covering both the theoretical and practical aspects of network analysis. It valuable resource for students and researchers in systems biology, bioinformatics, and related fields.
Save
Covering the principles and the basic concepts of systems biology, topics such as network modeling computational analysis, and dynamic modeling are thoroughly addressed.
Provides a practical guide to statistical methods in bioinformatics, covering a wide range of topics including data analysis, hypothesis testing, and machine learning. It valuable resource for students and researchers in bioinformatics and related fields.
Save
Provides an overview of data analysis methods used in systems biology. It covers topics such as data normalization, clustering, and dimensionality reduction. It useful reference for students and researchers in the field.
Widely regarded as one of the best textbooks of Cell Biology, this book provides a solid foundation in biology for students and researchers, especially those who lack a background in the field.
Provides an introduction to biological networks and systems, covering a wide range of topics including network structure, network dynamics, and network analysis. It valuable resource for students and researchers in network biology and related fields.
Provides an accessible introduction to the molecular basis of life, explaining evolution, genomics, and the latest techniques used in genetic analysis.
Save
Provides a concise and accessible introduction to the field of bioinformatics, covering topics such as sequence alignment, gene expression analysis, and protein structure prediction.
Provides a comprehensive overview of bioinformatics. It covers topics such as sequence analysis, gene expression analysis, and protein structure prediction. It useful reference for students and researchers in the field.
Save
Provides a concise and accessible introduction to Bayesian statistics, which powerful statistical approach that is well-suited for analyzing biological data.
Save
Provides an overview of network medicine. It covers topics such as network topology, network dynamics, and network-based drug discovery. It useful reference for students and researchers in the field.
Provides an introduction to bioinformatics. It covers topics such as sequence analysis, gene expression analysis, and protein structure prediction. It useful reference for students and researchers in the field.
Save
Provides an overview of data mining methods used in bioinformatics. It covers topics such as data preprocessing, clustering, and classification. It useful reference for students and researchers in the field.
Provides a comprehensive overview of molecular biology. It covers topics such as DNA structure, gene expression, and protein synthesis. It useful reference for students and researchers in the field.
Save
Provides a comprehensive overview of cell biology. It covers topics such as cell structure, cell function, and cell division. It useful reference for students and researchers in the field.
Provides a comprehensive overview of biochemistry. It covers topics such as protein structure, enzyme catalysis, and metabolism. It useful reference for students and researchers in the field.
Provides an introduction to bioinformatics. It covers topics such as sequence analysis, gene expression analysis, and protein structure prediction. It useful reference for students and researchers in the field.
Provides an introduction to systems biology. It covers topics such as systems thinking, modeling, and simulation. It useful reference for students and researchers in the field.
Provides a comprehensive overview of network analysis. It covers topics such as network topology, network dynamics, and network visualization. It useful reference for students and researchers in the field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/g8f8zs/network
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Effort
6-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Icahn School of Medicine at Mount Sinai
Instructor
Avi Ma’ayan, PhD
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Systems Biology and Biotechnology. It's comprised of six courses:
- Experimental Methods in Systems Biology
- Introduction to Systems Biology
- Network Analysis in Systems Biology (viewing)
- Integrated Analysis in Systems Biology
- Dynamical Modeling Methods for Systems Biology
- Systems Biology and Biotechnology Capstone
Good to know
Examines data analysis methods commonly used in systems biology, bioinformatics, and systems pharmacology disciplines
Uses real-world datasets and practical exercises to solidify understanding of data analysis methods
Introduces mathematical concepts underpinning data analysis methods and workflows
Emphasizes the importance of data analysis in modern biological and medical research
Incorporates tools developed by the Ma'ayan Laboratory at Mount Sinai for data analysis and visualization
May require additional software or resources not readily available to all learners
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