Analysing CRISPR Screens with edgeR
Welcome to the “Analyzing CRISPR Screens with edgeR”. Our aim is to empower researchers like you with the tools and knowledge needed to navigate the complex landscape of CRISPR data analysis.
This platform serves as the central hub for a comprehensive guide on leveraging one of the most commonly used differential expression analysis Bioconductor package edgeR, for the analysis of CRISPR screens. Whether you’re delving into CRISPR experiments for the first time or seeking advanced insights, this guide will equip you with essential skills and knowledge to extract meaningful information from your data using powerful and robust statistical methods presented in edgeR.
Here we provide you with a robust foundation in the analysis of CRISPR data using edgeR. Throughout this guide, we walk you through various steps of typical CRISPR analysis workflows using example data set to count the single guide RNAs (sgRNAs) from your sequencing files (fastq, etc.), pre-process the count matrices (filtering, normalising, etc.), fit a statistical model to identify the hit guides, genes and pathways, and visualize the results.
This guide is crafted with both novice and experienced researchers in mind. Whether you’re an experimental biologist stepping into the realm of CRISPR data or a seasoned bioinformatician seeking insights into edgeR in the context of CRISPR gene editing, you’ll find valuable content here. We make minimal assumptions about your previous programming or statistical experience, aiming to create a resource that is accessible to a broad audience.
We welcome feedback from all users to improve this guide continually, enhancing accessibility and refining technical details. Your input is instrumental in making this resource more valuable to the community.