[["index.html", "Orchestrating Single-Cell Analysis with Bioconductor Welcome What you will learn What you won’t learn Who we wrote this for Why we wrote this", " Orchestrating Single-Cell Analysis with Bioconductor Authors: Robert Amezquita [aut], Aaron Lun [aut], Stephanie Hicks [aut], Raphael Gottardo [aut], Alan O’Callaghan [cre] Version: 1.20.0 Modified: 2022-09-05 Compiled: 2025-11-03 Environment: R version 4.5.1 Patched (2025-08-23 r88802), Bioconductor 3.22 License: CC BY 4.0 Copyright: Bioconductor, 2020 Source: https://github.com/OSCA-source/OSCA Welcome This is the landing page for the “Orchestrating Single-Cell Analysis with Bioconductor” book, which teaches users some common workflows for the analysis of single-cell RNA-seq data (scRNA-seq). This book will show you how to make use of cutting-edge Bioconductor tools to process, analyze, visualize, and explore scRNA-seq data. Additionally, it serves as an online companion for the paper of the same name. What you will learn The goal of this book is to provide a solid foundation in the usage of Bioconductor tools for single-cell RNA-seq analysis by walking through various steps of typical workflows using example datasets. We strive to tackle key concepts covered in the manuscript, “Orchestrating Single-Cell Analysis with Bioconductor”, with each workflow covering these in varying detail, as well as essential preliminaries that are important for following along with the workflows on your own. What you won’t learn The field of bioinformatic analysis is large and filled with many potential trajectories depending on the biological system being studied and technology being deployed. Here, we only briefly survey some of the many tools available for the analysis of scRNA-seq, focusing on Bioconductor packages. It is impossible to thoroughly review the plethora of tools available through R and Bioconductor for biological analysis in one book, but we hope to provide the means for further exploration on your own. Thus, it goes without saying that you may not learn the optimal workflow for your own data from our examples - while we strive to provide high quality templates, they should be treated as just that - a template from which to extend upon for your own analyses. Who we wrote this for We’ve written this book with the interested experimental biologist in mind, and do our best to make few assumptions on previous programming or statistical experience. Likewise, we also welcome more seasoned bioinformaticians who are looking for a starting point from which to dive into single-cell RNA-seq analysis. As such, we welcome any and all feedback for improving this book to help increase accessibility and refine technical details. Why we wrote this This book was conceived in the fall of 2018, as single-cell RNA-seq analysis continued its rise in prominence in the field of biology. With its rapid growth, and the ongoing developments within Bioconductor tailored specifically for scRNA-seq, it became apparent that an update to the Orchestrating high-throughput genomic analysis with Bioconductor paper was necessary for the age of single-cell studies. We strive to highlight the fantastic software by people who call Bioconductor home for their tools, and in the process hope to showcase the Bioconductor community at large in continually pushing forward the field of biological analysis. "],["book-contents.html", "Book contents Introduction Basics Advanced Multi-sample Workflows", " Book contents The OSCA book is actually a collection of several sub-books spanning a variety of topics and different levels of assumed reader knowledge. Each sub-book is generated using bookdown, compiled twice a week to ensure that the examples still run on the current R/Bioconductor code base. Interlinking between related topics enable readers to seamlessly transition within a sub-book, and even between different sub-books. To get started, click on the links below to navigate to each sub-book. Introduction This describes how to install R and Bioconductor packages, links out to some resources to learn R, describes how to load datasets into an R session, provides an overview of the SingleCellExperiment class, and performs a “quick start” demonstration for basic single-cell RNA-seq analyses. It is intended for readers with little-to-no computational background who are just getting started with analyses in R. Basics This describes the steps of a simple single-cell RNA-seq analysis, involving quality control, normalization, various forms of dimensionality reduction, clustering into subpopulations, detection of marker genes, and annotation of cell types. It is intended for users who already have some familiarity with R and want to get hands-on with some basic single-cell analyses. Advanced This describes the more complex steps of a single-cell RNA-seq analysis ranging from doublet detection, cell cycle assignment, specific steps for processing droplet data, nuclei-specific analyses, trajectory analyses, integrated analyses with protein abundances, and interactive visualization. It also elaborates on some of the basic analysis steps, focusing on alternative strategies and theoretical considerations. It is intended for readers who are already familiar with basic single-cell analyses, possibly after reading some of the prior books in this collection. Multi-sample This describes the handling of multiple samples in a single-cell RNA-seq analysis, starting with integration of multiple datasets into a common space for consistent analyses, differential expression comparisons between conditions based on pseudo-bulk samples, and differential abundance analyses for cell subpopulations. It is intended for readers who are already familiar with basic single-cell analyses, possibly after reading some of the prior books in this collection. Workflows This contains worked case studies of analyses of a variety of single-cell datasets, each proceeding from a SingleCellExperiment object. Exposition is generally minimal other than for dataset-specific justifications for parameter tweaks; refer to the other books in the OCSA collection for a detailed explanation of the theoretical basis of each step. It is intended for readers who already know the background and just want some code to copy and paste into their own analyses. "],["contributors.html", "Contributors", " Contributors .rebook-collapse { background-color: #eee; color: #444; cursor: pointer; padding: 18px; width: 100%; border: none; text-align: left; outline: none; font-size: 15px; } .rebook-content { padding: 0 18px; display: none; overflow: hidden; background-color: #f1f1f1; } Aaron Lun, PhD When one thinks of single-cell bioinformatics, one thinks of several titans who bestride the field. Unfortunately, they weren’t available, so we had to make do with Aaron instead. He likes long walks on the beach (as long as there’s Wifi) and travelling (but only in business class). His friends say that he is “absolutely insane” and “needs to get a life”, or they would if they weren’t mostly imaginary. His GitHub account is his Facebook and his Slack is his Twitter. He maintains more Bioconductor packages than he has phone numbers on his cell. He has a recurring event on his Google Calendar to fold his laundry. He is… the most boring man in the world. (“I don’t often cry when I watch anime, but when I do, my tears taste like Dos Equis.”) He currently works as a Scientist at Genentech after a stint as a research associate in John Marioni’s group at the CRUK Cambridge Institute, which was preceded by a PhD with Gordon Smyth at the Walter and Eliza Hall Institute for Medical Research. Robert Amezquita, PhD Robert Amezquita is a Postdoctoral Fellow in the Immunotherapy Integrated Research Center (IIRC) at Fred Hutch under the mentorship of Raphael Gottardo. His current research focuses on utilizing computational approaches leveraging transcriptional and epigenomic profiling at single-cell resolution to understand how novel anti-cancer therapeutics - ranging from small molecule therapies to biologics such as CAR-T cells - affect immune response dynamics. Extending from his graduate work at Yale’s Dept. of Immunobiology, Robert’s research aims to better understand the process of immune cell differentiation under the duress of cancer as a means to inform future immunotherapies. To accomplish this, Robert works collaboratively across the Fred Hutch IIRC with experimental collaborators, extensively utilizing R and Bioconductor for data analysis. Stephanie Hicks, PhD Stephanie Hicks is an Assistant Professor in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health. Her research interests focus around developing statistical methods, tools and software for the analysis of genomics data. Specifically, her research addresses statistical challenges in epigenomics, functional genomics and single-cell genomics such as the pre-processing, normalization, analysis of noisy high-throughput data leading to an improved quantification and understanding of biological variability. She actively contributes software packages to Bioconductor and is involved in teaching courses for data science and the analysis of genomics data. She is also a faculty member of the Johns Hopkins Data Science Lab, co-host of The Corresponding Author podcast and co-founder of R-Ladies Baltimore. For more information, please see http://www.stephaniehicks.com Raphael Gottardo, PhD Raphael Gottardo is the Scientific Director of the Translational Data Science Integrated Research Center (TDS IRC) at Fred Hutch, J. Ordin Edson Foundation Endowed Chair, and Full Member within the Vaccine and Infectious Disease and Public Health Sciences Division. A pioneer in developing and applying statistical methods and software tools to distill actionable insights from large and complex biological data sets.In partnership with scientists and clinicians, he works to understand such diseases as cancer, HIV, malaria, and tuberculosis and inform the development of vaccines and treatments. He is a leader in forming interdisciplinary collaborations across the Hutch, as well as nationally and internationally, to address important research questions, particularly in the areas of vaccine research, human immunology, and immunotherapy. As director of the Translational Data Science Integrated Research Center, he fosters interaction between the Hutch’s experimental and clinical researchers and their computational and quantitative science colleagues with the goal of transforming patient care through data-driven research. Dr. Gottardo partners closely with the cancer immunotherapy program at Fred Hutch to improve treatments. For example, his team is harnessing cutting-edge computational methods to determine how cancers evade immunotherapy. He has made significant contributions to vaccine research and is the principal investigator of the Vaccine and Immunology Statistical Center of the Collaboration for AIDS Vaccine Discovery. Acknowledgements We thank the Bioconductor core team and the emerging targets subcommittee for commissioning this work, along with all our contributors to the companion manuscript of this book. For the book itself, there have been many contributors over the years, and I probably haven’t remembered them all, so don’t be offended if you don’t spot yourself below: Levi Waldron (City University of New York, USA), for advice on the code-related aspects of managing the online version of this book. Kevin Rue-Albrecht (University of Oxford, United Kingdom), for contributing the interactive data analysis chapter. Charlotte Soneson (Friedrich Miescher Institute, Switzerland), for many formatting and typographical fixes. Al J Abadi (University of Melbourne, Australia), for bringing the log-normalization corner case to our attention. Philippe Boileau (University of California Berkeley, USA), for demonstrations on how to use scPCA. Pierre-Luc Germain (ETH Zürich), for improvements to the doublet chapter involving scDblFinder. Peter Hickey (WEHI, Australia), for spotting and fixing many typos. Finally, we would like to thank all Bioconductor contributors for their efforts in creating the definitive leading-edge repository of software for biological analysis. It is truly extraordinary to chart the growth of Bioconductor over the years. We are thankful for the wonderful community of scientists and developers alike that together make the Bioconductor community special. "]]