About the content
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.
How Did Yeast Become a Wine Maker? (Clustering Algorithms)
- An Evolutionary History of Wine Making
- Identifying Genes Responsible for the Diauxic Shift
- Introduction to Clustering
- k-Means Clustering
- The Lloyd Algorithm
- Clustering Genes Implicated in the Diauxic Shift
- Limitations of k-Means Clustering
- From Coin Flipping to k-Means Clustering
- Making Soft Decisions in Coin Flipping
- Soft k-Means Clustering
- Hierarchical Clustering
- Epilogue: Clustering Tumor Samples
What Genetic Characteristics Do Human Populations Share? (Principal Components Analysis)
- Specific Content TBA
Bioinformatics Application Challenge: Clustering Biological Big Data (RNA-seq)
Department of Computer Science and Engineering
Department of Computer Science & Engineering
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