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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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Single cell Expression Atlas and single cell data submission

Trainers: Liora Vilmovsky

Overview: The trainers will guide us through exploring gene expression data with a lecture and practical exercises.

Materials

  • Presentation slides
  • Single cell Expression Atlas webpage
  • Annotare submission tool webpage

Activity

  • Single Cell Expression Atlas exercises
  • Annotare exercise
  • Sc-RNA samples
  • 10xV3_RNAseq_exp_info
  • fastaq-folder
  • page navigation-left-circle-1_1 QC, pre-processing and normalisation
  • pageAnalysing data navigation-right-circle-1_1