Code developed from Yale's Krishnaswamy Lab during their January 2021 Machine Learning for Single Cell Analysis workshop.
The purpose of this workshop is to tear back the complexity behind single cell analysis. Participants will learn practical skills for analyzing single cell datasets and develop a conceptual understanding of the machine learning foundations behind each method. Participants will also receive an introduction to emerging trends in single cell analysis such as deep learning.
Each day, attendees will hear lectures from instructors with experience developing and applying single cell methods followed by intensive hands-on lab sessions with a 10:1 student:instructor ratio. In these lab sessions, participants will work in teams to analyze real-world single cell datasets. The workshop will include a bring-your-own-data sessions where students will have the opportunity to bring in their own experimental datasets (or use one we provide) and collaborate with students and instructors on their projects.
By the end of the course, students will:
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Understand the common workflow of a single cell experiment
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Be able to apply common machine learning methods for analysis of single cell data
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Grasp the impact of method choice and parameter selection on analysis
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Build a foundation for exploring the single cell literature