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
• Repo contents
• From raw reads to feature selection
• Dimensionality reduction, clustering, and annotation
• Batch correction and data integration
• Group Projects

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