Who should attend?
PhD students and Researchers working with single-cell data, eager to begin their journey in single-cell data analysis and deepen their understanding of underlying workflow.
How should you prepare?
No preparation is needed but to make most of this workshop, please bring any single-cell data (count matrices) you might already have.
What will we cover?
Overview of Single-Cell RNA-Seq Data Journey
- Overview of the end-to-end single-cell analysis workflow and discussion on the importance of each step in single-cell research
Understanding Data Formats
- Introduction to common single-cell data formats (e.x., FASTQ, MTX, H5AD) and explanation of metadata and its role in data analysis
Basics of Single-Cell Data Analysis
- Key analysis parameters: quality control thresholds, normalization methods
- Introduction to dimensionality reduction techniques (PCA, UMAP) and how parameter adjustments affect analysis outcomes
Handling Data Lifecycle
- Techniques for mixing and splitting cells across different conditions and best practices for data integration and batch effect correction
Exploring UMAPs and Clusters
- Generating UMAP plots and interpreting clusters
- Assessing data quality using marker genes
- Introduction to clustering algorithms and their biological significance
Data Annotation
- Strategies for annotating cell types and states and utilizing reference datasets and annotation tools
- Importance of accurate annotation for downstream analysis
Differential Gene Expression
- Methods for identifying differentially expressed genes
- Statistical considerations and multiple testing correction
- Interpreting results in a biological context
Hands-On Session with Nygen - Our Analysis Workbench and Insights Feature
- Step-by-step guidance through the analysis workflow using participants' data
- Practical exercises covering data import, processing, and visualization and using LLM-augmented insights feature
Highlighting Open-Source Tools for Advanced Users
- Brief overview of popular tools like Seurat and Scanpy and comparison with Nygen's capabilities
- Resources for further exploration and learning
Q&A and Closing Remarks
- Open session for participant questions and discussions
- Recap of key learnings and takeaways
Why BYOD?
This workshop will help you understand the end-to-end single cell analysis workflow using intuitive, no-code tools such as Nygen to accelerate your research.
Data Privacy Notice
For this demonstration, we will be analyzing data on Nygen’s cloud based analytics workbench which is trusted by over 750 leading scientists worldwide. Built on ISO 27001 ready infrastructure across 22 AWS regions, Nygen ensures end-to-end data security and industry compliance.
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