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Single-cell RNA-seq analysis using R

  • Overview
  • Search
  • Course content
  • Planning the experiment
    • Keynote: Cellular heterogeneity in liver and drivers of disease
    • Droplet-based library generation overview
    • Pipeline overview
    • Experimental design
  • Processing data
    • Getting set up: infrastructure terms
    • Raw reads to expression matrix
    • Cell atlases & interpretation
  • Analysing data
    • Designing your analysis
    • Pre-processing and dimensionality reduction
    • From UMAP to annotation
    • Online tools and follow-up
  • Technical help sheets
  • Trainers biographies
  • Further learning
  • Your feedback

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Keynote: Cellular heterogeneity in liver and drivers of disease

Trainer: Tallulah Andrews

Overview: In this session, I will present my recent work examining healthy and diseased human liver with single-cell and spatial transcriptomics.

Materials:

Lecture

Further Reading:

  • Comparison of scRNAseq technologies
  • Comparison of scRNAseq technologies2
  • Spike-in normalisation
  • Isoform quantification
  • Lipid-based cell-hashing
  • Single-cell vs single-nuc
  • Simple introduction

  • page navigation-left-circle-1_1 Planning the experiment
  • pageDroplet-based library generation overview navigation-right-circle-1_1