Room 050
Wednesday June 25th 2025
13.50 – 15.20
Workshop: How to Better Computationally Analyze Your Single-Cell RNA Sequencing Data
Are you working with single-cell RNA sequencing data or planning to soon?
This hands-on workshop is designed specifically for researchers, biologists, experimentalists and data analysts who are either new to scRNA-seq analysis or looking to strengthen their computational pipelines for data analysis skills.
Whether you're just getting started or ready to take your analysis to the next level, this workshop will give you the tools and confidence to work smarter with scRNA-seq data.
By the end of this workshop you'll be able to:
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Understand and explain the full scRNA-seq data analysis pipeline.
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Confidently apply the course analysis of scRNA-seq data from extraction to cluster maps and on other datasets.
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Recognize decision-making steps along the analysis pipeline and justify your choices.
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Define best practice for managing cellular resolution data.
Workshop requirements
A solid grasp of molecular biology (DNA, RNA, gene expression, PCR, etc.) will help you get the most out of the session. Some experience with command line interfaces (like Linux or Mac OS X) and basic knowledge of object-oriented programing in Python or R is recommended.
Please note: To be considered for a workshop place you MUST already have an approved place at The Festival of Genomics and Biodata.
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Workshop Format:
13:50 Introductions, aims and overview of steps involved in taking raw single-cell RNA-seq data
14:05 Visualizing and Interpreting scRNA-seq Data
- Overview of the main steps in the scRNA-Seq analysis workflow
- Visualizations for quality control
- Visualization of annotated cell type clusters and results
- Discussion of downstream methods
Mary Piper, Senior Principal Computational Biologist, Pfizer & Meeta Mistry, Associate Director, Harvard Chan Bioinformatics Core
14.25 Benchmarking Machine Learning Methods to Integrate Single-Cell Data to Build a Tissue-Specific Atlas
- Introduction to Single-Cell Data and the Need for Integration
- Why Benchmarking is Critical
- Overview of Machine Learning Approaches for Integration
- Key Metrics for Performance Evaluation
- Biological and Computational Challenges
- The audience will understand the importance of data integration to benchmark and evaluate integration methods
Zhaleh Safikhani, Principal Machine Learning Research Scientist, AbbVie
14:45 Advanced Analysis Techniques with Real-World Examples
This session will showcase computational methods that are useful for underpinning new insights, introduce the methods and show you where you can access more information.
- Population scale single cell sequencing to understand regulation and variation of the human immune system.
- Multiplexing and demultiplexing to enable profiling of large cohorts
- Analysis considerations for large cohorts
- Leveraging single cell genomics with human genetics to provide novel insights into disease mechanisms.
Gracie Gordon, Computational Scientist 3, Genentech