Preprocessing and Quality Control

This chapter delves into the critical steps of data preprocessing and quality control (QC) in single-cell RNA sequencing (scRNA-seq) analysis. It begins with an overview of scRNA-seq technology, highlighting recent advancements and the inherent challenges posed by systematic and random noise in the data. The chapter emphasizes the importance of QC metrics, such as sequencing depth, gene count, and mitochondrial gene content, to filter out poor-quality cells. Techniques for identifying and removing doublets, correcting batch effects, and addressing technical artifacts are discussed. Key preprocessing steps, including normalization, gene filtering, and dimensionality reduction methods like PCA, t-SNE, and UMAP, are covered. Additionally, the chapter reviews popular software tools for implementing these processes. A case study using Python and Scanpy illustrates the practical application of these techniques, providing a comprehensive guide for ensuring high-quality scRNA-seq data analysis.