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Starts 8 June 2025 14:40
Ends 8 June 2025
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18 minutes
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Overview
Discover powerful techniques for case-control analysis of spatially resolved molecular data, focusing on accurate methods developed at the Raychaudhuri Lab.
Syllabus
- Introduction to Case-Control Analysis
- Basics of Spatially Resolved Molecular Data
- Key Statistical Methods for Case-Control Analysis
- Analytical Approaches in Spatial Data
- Case Studies from Raychaudhuri Lab
- Software and Tools
- Advanced Methods and Challenges
- Evaluation of Spatial Models
- Conclusion and Future Directions
- Final Project
Overview of Case-Control Studies
Importance in Molecular Data Analysis
Types of Spatially Resolved Data
Techniques and Tools for Data Acquisition
Logistic Regression
Conditional Logistic Regression
Feature Selection Techniques
Spatial Statistics
Spatial Autocorrelation Analysis
Spatial Interpolation Methods
Analysis of Spatial Data in Immunology
Applications in Genetic Studies
Introduction to Relevant Software (e.g., R, Python)
Practical Sessions with Real Datasets
Visualization Techniques for Spatial Data
Handling High-Dimensional Data
Dealing with Missing Data in Spatial Contexts
Recent Advances in Spatial Statistics
Model Validation Techniques
Performance Metrics for Spatial Models
Emerging Trends in Spatial Molecular Data Analysis
Future Research Opportunities
Design and Conduct a Case-Control Study
Analysis and Presentation of Findings
Subjects
Data Science
