Learning Single-Cell Analysis with Theis Lab: 01. Preface

· 4 min read · Analysis Notes

Learning Single-Cell Analysis with Theis Lab: 01. Preface

"The best time to start learning was ten years ago. The second best time is now."

Hi everyone! I'm Miana, a Biology PhD student. I recently decided to launch a new series — Learning Single-Cell Analysis with Theis Lab — which will be a Chinese translation and adaptation of Theis Lab's Single-Cell Best Practices tutorial.

🎯 A Few Thoughts

Why start this series? It goes back to my own experience. When I first got into single-cell analysis, I chose R and Seurat — I already had an R background, and ggplot produces beautiful visualizations. But as the datasets grew larger, R's computational speed and memory consumption became real bottlenecks — even on a server, I frequently ran into limits. Another frustration: many of the latest analysis tools are being developed in Python, and constantly converting data formats between R and Python is tedious and error-prone.

That said, I've always believed there's value in being comfortable with multiple ecosystems — the visualization and user-friendliness of R (especially Seurat), and the computational efficiency and scalability of Python (especially scanpy), are both well worth learning. These experiences made me realize the need for a systematic, up-to-date tutorial.

While searching for study materials, I noticed that high-quality Chinese-language tutorials were scarce. There are scattered blog posts and video tutorials, but they tend to cover only a single analysis step without providing a systematic overview. English-language resources, on the other hand, can feel dense and intimidating — especially for beginners.

Then I discovered this tutorial from Theis Lab. The expert authorship, the accessible writing style, and the breadth of topics covered immediately won me over. Sharing it with the community is exactly what motivated this series.

🤔 Why This Tutorial?

If you've been exploring the single-cell field, you've probably noticed that online tutorials tend to be either too basic or too specialized — it's hard to find something that is both systematic and approachable. Single-Cell Best Practices fills that gap perfectly:

1. The most current and comprehensive professional guide: First published in 2023, this tutorial is continuously updated and maintained by one of the top laboratories in the single-cell field. From foundational concepts to advanced analyses, from data preprocessing to downstream interpretation, it covers every aspect of single-cell analysis in meticulous detail.

2. Built on the Python ecosystem: The tutorial is primarily based on the scanpy package. In the era of AI and big data, Python's advantages are becoming increasingly clear:

  • Powerful data processing capabilities
  • A rich machine learning toolkit
  • An active open-source community
  • A mature visualization ecosystem

3. A continuously updated knowledge base: Unlike one-off technical blog posts, this series will be steadily updated to keep pace with the latest developments in the field. I will regularly check for updates to the original tutorial to ensure readers always have access to current knowledge.

📚 Content Plan

This series will cover all chapters of the original tutorial, including but not limited to:

  • Single-cell sequencing fundamentals
  • Data quality control and preprocessing
  • Data integration and batch-effect correction
  • Dimensionality reduction and clustering
  • Trajectory inference
  • Differential expression analysis
  • …and more

While maintaining scientific accuracy, I will use clear, accessible language and supplement the material with practical examples and background knowledge to help readers deepen their understanding.

📅 Update Schedule

To ensure quality while staying compatible with my own study and work schedule, I plan to:

  • Publish 1–2 articles per week
  • Prioritize the most fundamental and important chapters first
  • Adjust the update frequency and level of detail based on reader feedback

🔗 Resources

Original tutorial: Single-Cell Best Practices by Theis Lab

Each installment will also include a keyword you can use to obtain the original English PDF, code, and datasets for that chapter.

  • The precursor to this book: Current best practices in single-cell RNA-seq analysis: a tutorial
  • The Bioconductor single-cell tutorial (based on SingleCellExperiment): Orchestrating Single-Cell Analysis with Bioconductor
  • The book's recommended Python primer: Automate the Boring Stuff with Python
  • The book's recommended R primer: R for Data Science, 2e

⚖️ Copyright Notice

This article series is a Chinese translation and adaptation of Theis Lab's Single-Cell Best Practices. The original tutorial is released under the Apache 2.0 License. We would like to state:

  • All original content is copyright of the original authors.
  • The translated content is intended for educational and non-commercial use only.
  • If you spot any inaccuracies, please let us know!

💌 Closing Words

If you found this helpful, please like, bookmark, and share! Questions and suggestions are always welcome in the comments.

#Bioinformatics #SingleCellSequencing #Tutorial #Python #Scanpy

Originally posted on WeChat Official Account: bioinfo_studyroom

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