Dimension reduction for biology data
Winter Workshop Series: Dimension reduction for biology data
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Instructor: Alex Diaz-Papkovich
March 4, 2021 at 1pm
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***To provide the best environment for learning, space is limited. Please make sure you will be able to attend the workshop, otherwise you may not be allowed to register again this session.***
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Modern -omics datasets are very highly dimensional, covering samples (cells, people, bacteria, etc) across thousands to millions of variables (SNPs, genes, proteins, etc). Participants will learn about methods like PCA, MDS, t-SNE, and UMAP, and their applications in investigating data that lives in high dimensions.
We will cover the motivations and ideas behind the methods and go over examples using both artificial and real-world data. By the end of the session, you will be able to code implementations of dimension reduction as well as visualize your data in useful ways and consider approaches for downstream analysis.
The methods covered have implementations in Python and R, and the course will be taught using Python notebooks (with some equivalent code in R provided).
There will be some linear algebra, analysis, and statistics -- knowledge of these topics will be useful for but not critical to participation.
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Code of Conduct
Our event is dedicated to providing a harassment-free conference experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), or technology choices. We do not tolerate harassment of conference participants in any form. Event participants violating these rules may be sanctioned or expelled from the event without a refund at the discretion of the conference organizers.
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