pdpm-workshop-2025

A beginner‑friendly course on single‑cell RNA‑seq analysis, covering data characteristics, Scanpy workflow, manifold‑learning‑based dimensionality reduction, and trajectory inference with key mathematical concepts and practical examples.

Table of Contents

1. Course Overview
2. Prerequisites
3. Course Outline
   1. Introduction to Single‑Cell Data
   2. Scanpy Workflow
   3. Manifold Learning & Dimensionality Reduction
      • PCA
      • t‑SNE
      • UMAP
      • Diffusion Maps
   4. Trajectory Inference
      • Diffusion Pseudotime (DPT)
      • PAGA
      • Other Methods (Monocle3, Slingshot)
   5. Hands‑On Session
   6. Summary & Next Steps
4. Recommended Reading
5. License & Acknowledgements

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Course Overview

This workshop will guide you through the fundamentals of single‑cell RNA‑seq analysis:

• Understand the unique challenges of high-dimensional, sparse single‑cell data
• Learn a complete Scanpy-based preprocessing and analysis pipeline
• Dive into the mathematical foundations of popular nonlinear dimensionality‑reduction techniques
• Explore trajectory inference approaches to reconstruct developmental or differentiation pathways
• Work through hands‑on examples with real datasets

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Prerequisites

• Basic familiarity with Python (variables, functions, pip)
• Fundamental understanding of gene expression and RNA‑seq concepts
• A laptop with Python ≥ 3.8 installed

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Course Outline

1. Introduction to Single‑Cell Data

• Characteristics of single‑cell RNA‑seq
• Technical noise, dropouts, and batch effects
• Biological heterogeneity and lineage concepts

2. Scanpy Workflow

• AnnData structure (.X, .obs, .var)
• Quality control and filtering
• Normalization, log‐transformation, and HVG selection
• Neighborhood graph construction

3. Manifold Learning & Dimensionality Reduction

• PCA: covariance matrix, eigen decomposition
• t‑SNE: pairwise affinities, KL divergence
• UMAP: fuzzy simplicial sets, cross‐entropy loss
• Diffusion Maps: Markov transition matrices, spectral embedding

4. Trajectory Inference

• Concept of pseudotime
• Diffusion Pseudotime (DPT): diffusion distance, root cell selection
• PAGA: cluster graph abstraction, connectivity metrics
• Overview of Monocle3, Slingshot, SCORPIUS

5. Hands‑On Session

• Loading 10x Genomics demo data
• Running the full Scanpy pipeline end‑to‑end
• Visualizing embeddings and lineage trajectories
• Q&A with live debugging

Recommended Reading

• Wolf, F. A., Angerer, P., & Theis, F. J. (2018). SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19, 15.
• Becht, E., McInnes, L., Healy, J., et al. (2019). Dimensionality reduction for visualizing single-cell data using UMAP. Nature Biotechnology 37, 38–44.
• van Dijk, D., Sharma, R., Nainys, J., et al. (2018). Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell 174, 716–729.e27.
• Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016). Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods 13, 845–848.
• McInnes, L., Healy, J., & Melville, J. (2020). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint arXiv:1802.03426.

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License & Acknowledgements

This workshop materials are released under the MIT License.
Thanks to the Scanpy development team and the single‑cell analysis community for their ongoing contributions.