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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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Designing your analysis

Trainer: Tallulah Andrews

Overview: In the training session I will be discussing the pros and cons of different single-cell RNAseq platforms and how to design your experiment to avoid confounding factors. In addition, I will discuss when and where to apply different analysis tools and how inappropriate use of these tools can lead to false conclusions.

Materials

  • Recorded lecture
  • Presentation slides
  • page navigation-left-circle-1_1 Analysing data
  • pageDimensionality reduction, clustering, and annotation navigation-right-circle-1_1