/scrnaseq_python_2023

Materials for the EBI course "Single-cell RNA-seq analysis using Python 2023"

Primary LanguageJupyter Notebook

Single-cell RNA-seq analysis using Python 2023

Materials for the EBI course "Single-cell RNA-seq analysis using Python 2023"

Materials adapted from: Single-cell best practices

Table of contents

Overview

This repositoty contains all the hands-on materials taught in the course "Single-cell RNA-seq analysis using Python 2023". Here, we learn how to analyse single-cell data starting from raw reads until the cell-type annotation of our data. Moreover, we explore and perform batch correction and data integration methods for our data. The hands-on materials include practicals(demos), exercises with answer keys, and project exercises for further hands-on practice.

Repo contents

  • bin : scripts necessary for preprocessing analysis
  • envs : yml files, environments used for the practicals
  • exercises : jupyter notebooks with exercises based on the practicals and their answers
  • practicals : jupyter notebooks for each practical( demo, follow along tutorial for each session)
  • projects : Coming soon

From raw reads to feature selection

practical_1 notebook is a follow along tutorial for the preprocessing analysis of single-cell data. It includes the following steps :

  1. Raw Data Processing
    • Build the reference index (pyroe and salmon)
    • Perform mapping and quantification (alevin and alevin-fry)
    • remove empty drops (bioconductor-dropletutils)
    • Alternative method for raw data preprocessing (simpleleaf method)
  2. Quality Control (QC)
    • Filtering low quality barcodes(scanpy)
    • Correction of ambient RNA(SoupX)
    • Doublet Detection(scDblFinder)
  3. Normalisation methods
    • library size 10e4 and log-transform(scanpy)
    • scran-normalisation(scran)
  4. Feature Selection
    • Deviance (scry)
    • Variance(scanpy)

Dimensionality reduction, clustering, and annotation

Batch correction and data integration

Group Projects