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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
    • Multi-omics
    • Trajectory analysis
  • 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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Multi-omics

Trainer: Vinicius Maracaja-Coutinho

Overview: This session provides an introduction to the concept of multi-omics, where integration of various large datasets characterizing, identifying and/or quantifying biomolecules takes place. By taking an approach combining these datasets, deeper insights can be gained for biological phenomena.

Materials

Presentation slides

Recording

  • page navigation-left-circle-1_1 Abundance and differential expression
  • pageTrajectory analysis navigation-right-circle-1_1