Single-cell analysis MiniBootcamp

This MiniBootcamp focuses on the core stages of a typical single-cell data analysis, offering a clear, practical view of how single-cell datasets are transformed, analyzed, and interpreted. It highlights key analytical steps, common challenges, and established methods for working with complex, high-dimensional single-cell data.

Key topics include:

• Data transformation and normalization
• Feature filtering and batch-effect correction
• Dimensionality reduction
• Cell clustering, classification, and annotation
• Differential analysis
• Biological interpretation of patterns

You will gain a strong understanding of standard single-cell analysis techniques to confidently explore and interpret your own single-cell data, no matter what analysis tool you use.

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