Dimensionality Reduction and Clustering

This chapter explores the crucial techniques of dimensionality reduction and clustering in the context of single-cell RNA sequencing (scRNA-seq) data analysis. Dimensionality reduction, including methods like Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), and t-distributed Stochastic Neighbor Embedding (t-SNE), simplifies high-dimensional data for visualization and downstream analysis. Clustering, exemplified by methods like k-means and graph-based approaches groups cells with similar expression profiles to identify cell types and states, uncovering underlying biological structures. The chapter details PCA and t-SNE algorithms, their applications, and software tools, providing Python-based case studies to demonstrate their practical implementation in scRNA-seq data analysis.