This workshop will present a deep-dive into the computational workflows involved in processing scRNA-seq data. It will have hands-on demonstrations of the most common tasks and the tools available for addressing central biological questions. The workshop will provide guidelines regarding best practices for performing computational analyses.  

Target Audience and Pre-Requisites: 

The target audience are researchers who are generating, planning or working with single-cell RNA seq data. The workshop is specifically aimed at biologists, experimentalists or data analysts with little-to-medium experience in developing computational pipelines for data analysis.  

A good understanding of molecular biology (DNA, RNA, gene expression, PCR, ...) is assumed, as examples will be given in the context of this field. 

Some familiarity with a command line interface (e.g. Linux, Mac OS X) and a minimal understanding of object-oriented programming (with e.g. Python or R) is recommended. 

Key Takeaways: 

After this workshop, you should have a better understanding of how to: 

• Explain the steps in the scRNA-seq data analysis pipeline 

• Repeat the course analysis of scRNA-seq data from extraction to cluster maps on other datasets 

• Recognize decision-making steps along the analysis pipeline and justify your choices 

• Define best practice for managing cellular resolution data 

Format: 

11:35 Introductions, aims and overview of steps involved in taking raw single-cell RNA-seq data 

11:50 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 

12.10 Benchmarking Machine Learning Methods to Integrate Single-Cell Data to Build a Tissue-Specific Atlas  

Zhaleh Safikhani, Principal Machine Learning Research Scientist, AbbVie 

12:30 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