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What will we cover?
1. Single-Cell RNA-Seq Data Journey • Overview of scRNA-seq technologies • Significance of each step in the analysis workflow
2. Data Formats & Metadata • FASTQ, MTX, H5AD, and more• How to manage and interpret metadata
3. Quality Control & Normalization • Best practices for filtering and scaling data • Common pitfalls and troubleshooting tips
4. Dimensionality Reduction & Clustering • PCA, UMAP, t-SNE fundamentals • Biological relevance of clustering and subpopulation discovery
5. Data Integration & Batch Effect Correction • Approaches to merge datasets across conditions or experiments • Strategies for handling technical variations
6. Differential Gene Expression • Statistical testing, multiple-testing correction • Biological interpretation of gene expression changes
7. Cell Type Annotation & Reference Databases • Matching clusters to known cell populations • Leveraging public datasets for deeper insights
8. Advanced Exercises with Nygen & Other Tools • Brief overview of popular tools like Seurat and Scanpy and comparison with Nygen's capabilities • LLM-augmented insights and real-time data visualization
9. BYOD Sessions • Analyze your own data under expert guidance• Immediate feedback and customized troubleshooting
Learning Outcomes
By the end of this course, you will be able to:
• Process single-cell RNA-seq data from raw reads to biologically interpretable results
• Select appropriate analysis methods and tools (Seurat, Bioconductor, Scanpy, or Nygen)
• Interpret quality control metrics, clustering outputs, and differential expression results
• Integrate multiple datasets and correct for batch effects• Annotate your data confidently to uncover meaningful bio logical insights