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Analysis of single cell RNA-seq data

For Deverlopers

  • working directory /nesi/project/nesi02659/sc-rna
  • Use sc-RNA ( OR long name sc-RNA/2023-07-gimkl-2022a-R-bundle-Bioconductor-3.15-R-4.2.1) module which contains all of the required R packages

Data set

  • Data used in this workshop is based on CaronBourque2020 relating to pediatric leukemia, with four sample types, including:
    • pediatric Bone Marrow Mononuclear Cells (PBMMCs)
    • three tumour types: ETV6-RUNX1, HHD, PRE-T

Workflow for developers

flowchart TD
    id1["Sequencing QC"] --> id2["Read alignment, Feature Counting, Cell calling"] --> id3["QC"] --> id4["Count normalisation"] --> id5["Feature selection"] --> id6["Dimensionality reduction"] --> id7["Data set integration"] --> id8["Clustering"] --> id9["Cluster marker genes"] --> id10["DE between conditions"] --> id11["Differential abundance between conditions"]

Prerequisites

  • Inermediate level knowledge on R (programming language)
  • Familiarity with terminal and basic linux commands
  • Some knowledge on shell environment variables and for loops
  • Ability to use a terminal based text editor such as nano
    • This is not much of an issue as we are using JupyterHub which has a more friendlier text editor.
  • Intermediate level knowledge on Molecular Biology and Genetics

Recommended but not required