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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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Trajectory analysis

Trainer: Jonathan Peña Ávila

Overview: This session provides an introduction to the trajectory analysis, the approach by which we can take pseudotime and predict or otherwise infer cellular changes at the transcriptomic level and map them to biological processes including differentiation into various cell types.

Materials

Presentation slides

Recording

  • page navigation-left-circle-1_1 Multi-omics
  • pageTechnical help sheet navigation-right-circle-1_1