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Single cell RNA-seq analysis using Python

  • Overview
  • Search
  • Course Content
  • Planing your experiment
    • Wet-lab overview 
    • Dry-lab overview
    • Experimental design
  • Processing data
    • Raw reads to expression matrix
    • QC, pre-processing and normalisation
    • Single cell Expression Atlas and single cell data submission
  • Analysing data
    • Designing your analysis
    • Dimensionality reduction, clustering, and annotation
    • Batch correction and data integration
    • Spatial transcriptomics
    • Abundance and differential expression
  • Technical help sheet
  • Trainer biographies
  • Further learning
  • Your feedback

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All materials are free cultural works licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, except where further licensing details are provided.

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Batch correction and data integration

Trainer: Yuyao Song

Overview: In this session, you will learn about the principal of batch correction and data integration, what are the differences between them, how to perform them using popular algorithms, as well as getting some practical advise.

Materials

  • Recorded lecture
  • Presentation slides

Practical

  • GitHub repository
  • page navigation-left-circle-1_1 Dimensionality reduction, clustering, and annotation
  • pageSpatial transcriptomics navigation-right-circle-1_1