Yale Center for Biomedical Data Science & Krishnaswamy Lab Present: "Machine Learning for Single Cell Analysis"

Single cell methods, such as single cell RNA-sequencing, are becoming an increasingly popular way for scientists to probe the heterogeneity and dynamics of biological systems. However, analysis of single cell datasets is a challenging task. The data itself is large and noisy, and choosing the correct tools for analysis requires sifting through literally hundreds of published methods. Getting started with a new single cell project can be a daunting task.

We want to help.

The purpose of this three day workshop is to tear back some of the complexity behind single cell analysis. Students will learn practical skills for analyzing single cell datasets and develop a conceptual understanding of the machine learning foundations behind each method. Students will also receive an introduction to emerging trends in single cell analysis such as deep learning.

Each day, students will hear lectures from instructors with experience developing and applying single cell methods followed by intensive hands-on lab sessions. In these lab sessions, students will work in teams to analyze real-world single cell datasets. The workshop will conclude with a hands-on bring-your-own-data workshop where students will have the opportunity to bring in their own experimental datasets and collaborate with students and instructors on their projects.

By the end of the course, students will:

1. Understand the common workflow of a single cell experiment

2. Be able to apply common machine learning methods for analysis of single cell data

3. Grasp the impact of method choice and parameter selection on analysis

4. Build a foundation for exploring the single cell literature

For more information and registration visit: https://machinelearningsinglecellanalysis.eventbrite.com