SINGLE-CELL RNA-SEQ ANALYSIS IN R: A HANDS-ON GUIDE: HANDS-ON scRNA-seq, MULTI-OMICS, AND BIOCONDUCTOR WORKFLOWS FOR REAL DATA ANALYSIS (REAL-WORLD DATA SCIENCE WITH R)

Single-Cell RNA-Seq Analysis in R: A Hands-On Guide delivers a complete, end-to-end workflow for analyzing single-cell genomics data using modern R and Bioconductor tools.This is not a theory-heavy textbook. It is a practical, pipeline-driven guide designed for researchers, data scientists, and bioinformaticians who need to move from raw sequencing data to validated biological insight without guesswork.Inside this book, readers will learn how to:Build a scalable R environment for genomics analysisProcess raw scRNA-seq data from FASTQ to count matrixPerform rigorous quality control and filteringNormalize data and select highly informative genesApply PCA, UMAP, and t-SNE correctlyCluster cells and identify meaningful populationsConduct statistically sound differential expression analysisReconstruct cellular trajectories using pseudotimeCorrect batch effects and integrate multiple datasetsPerform multi-omics integration with RNA, ATAC, and proteomicsAnalyze spatial transcriptomics data in tissue contextBuild reproducible, automated pipelines for real-world projectsGenerate publication-ready figures and reportsThe book also includes real-world case studies in cancer biology, immunology, stem cell research, and drug response analysis demonstrating how these methods are applied in practice.Whether working with single datasets or large-scale multi-omics projects, this guide provides the tools and workflows required to produce reliable, reproducible, and publication-ready results.If you want to move beyond fragmented tutorials and build a complete, production-grade single-cell analysis workflow in R, this book delivers exactly that. Read more