Data Science for Biotechnology students¶

This book is written with KTH’s course CB2030, Systems biology in mind.

• Data Science for Biotechnology students
• Supervised Machine Learning
  • Introduction to Machine Learning
  • Regression
  • Regularization
  • Multiple Regression Example
  • Multiple Regression Analysis of KPNA2 Gene Expression
  • Classification
  • Validation
  • Support Vector Machines
  • Example of classification
  • Multi-Layer Perceptrons (MLPs)
• Statistics
  • Hypothesis Testing
  • Differential expression analysis of two inbred mouse strains
  • Multiple Hypothesis Corrections
  • q value calculations in a comparison of two mouse strains
  • Checking q values against a known truth
• Unsupervised Machine Learning
  • Principal Component Analysis (PCA)
  • PCA of single cells
  • Clustering
  • Cluster analysis of single cells
• Network Analysis
  • Pathway Analysis
  • Example of ORA and GSEA
  • Flux Balance Analysis
  • Curated and Automatically Reconstructed GEMs
• Appendix
  • Appendix: Some Maths

Run the notebooks¶

The notebooks in this book can be run directly on the KTH JupyterHub — no installation needed. Sign in with your hub account, and the material is copied into your own workspace. Following a link again later updates the material without discarding the changes you have made.

Open the whole book in JupyterLab

Each chapter with code also has its own launch buttons at the top of the page.

This work by Lukas Käll is licensed under CC BY 4.0