Genomic Data Science and Clustering (Bioinformatics V)

About this course: How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters. In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data. Finally, you will learn how to apply popular bioinformatics software tools to solve a real problem in clustering.

Who is this class for: This course is primarily aimed at undergraduate-level learners in computer science, biology, or a related field who are interested in learning about how the intersection of these two disciplines represents an important frontier in modern science.

Created by:  University of California, San Diego

• Taught by:  Pavel Pevzner, Professor

  Department of Computer Science and Engineering

• Taught by:  Phillip Compeau, Visiting Researcher

  Department of Computer Science & Engineering

Basic Info Course 5 of 7 in the Bioinformatics Specialization Level Beginner Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.2 stars Average User Rating 4.2See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of California, San Diego UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.

Syllabus


WEEK 1


Week 1: Introduction to Clustering Algorithms



<p>Welcome to class!</p><p>At the beginning of the class, we will see how algorithms for&nbsp;<strong>clustering&nbsp;</strong>a set of data points&nbsp;will help us determine how yeast became such good wine-makers. At the bottom of this email is the Bioinformatics Cartoon for this chapter, courtesy of <a href="http://bearandfox.com" target="_blank" title="Link: http://bearandfox.com">Randall Christopher</a> and serving as a chapter header in the Specialization's bestselling <a href="http://bioinformaticsalgorithms.com" target="_blank">print companion</a>. How did the monkey lose a wine-drinking contest to a tiny mammal? &nbsp;Why have Pavel and Phillip become cavemen? And will flipping a coin help them escape their eternal boredom until they can return to the present? Start learning to find out!</p><p><img width="550" alt="" src="http://bioinformaticsalgorithms.com/images/cover/clustering_cropped.jpg" title="Image: http://bioinformaticsalgorithms.com/images/cover/clustering_cropped.jpg"></p>


5 videos, 2 readings expand

1. Video: (Check Out Our Wacky Course Intro Video!)
2. Reading: Course Details
3. LTI Item: Stepik Interactive Text for Week 1
4. Video: Which Yeast Genes are Responsible for Wine Making?
5. Video: Gene Expression Matrices
6. Video: Clustering as an Optimization Problem
7. Video: The Lloyd Algorithm for k-Means Clustering
8. Reading: Week 1 FAQs (Optional)

Graded: Week 1 Quiz
Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 1

WEEK 2


Week 2: Advanced Clustering Techniques



<p>Welcome to week 2 of class!</p> <p>This week, we will see how we can move from a "hard" assignment of points to clusters toward a "soft" assignment that allows the boundaries of the clusters to blend. We will also see how to adapt the Lloyd algorithm that we encountered in the first week in order to produce an algorithm for soft clustering. We will also see another clustering algorithm called "hierarchical clustering" that groups objects into larger and larger clusters.</p>


5 videos, 1 reading expand

1. LTI Item: Stepik Interactive Text for Week 2
2. Video: From Hard to Soft Clustering
3. Video: From Coin Flipping to k-Means Clustering
4. Video: Expectation Maximization
5. Video: Soft k-Means Clustering
6. Video: Hierarchical Clustering
7. Reading: Week 2 FAQs (Optional)

Graded: Week 2 Quiz
Graded: Open in order to Sync Your Progress: Stepik Interactive Text for Week 2

WEEK 3


Week 3: Introductory Algorithms in Population Genetics



2 readings expand

1. Reading: Statement on This Week's Material
2. Reading: How Have Humans Populated the Earth?

Graded: Week 3 Quiz