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
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 biological insights
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