Hands-On Single-Cell CRISPR Screen Analysis

Welcome

Single-cell CRISPR screens (e.g., perturb-seq) combine CRISPR genome engineering and single-cell sequencing to survey the effects of genetic perturbations on individual cells. Single-cell CRISPR screens pose enormous potential for unraveling mechanisms underlying disease and accelerating drug discovery. However, the analysis of these screens presents considerable statistical and computational challenges. Hands-On Single-Cell CRISPR Screen Analysis is a step-by-step guide to carrying out statistically rigorous and massively scalable single-cell CRISPR screen data analysis using the sceptre R package.

sceptre is compatible with a broad range of single-cell CRISPR screen experimental designs. In particular, datasets can vary along the following axes: multiplicity of infection (low or high), genomic element targeted (gene or noncoding regulatory element), CRISPR modality (CRISPRko, CRISPRi, CRISPRa, or CRISPR prime or base editing), and molecular readout (gene expression, protein expression, or chromatin accessibility). Additionally, sceptre enables the analysis of massive-scale data that may be too large to fit into memory or that require a cluster or cloud to process.

Navigating this book

This book contains an introductory chapter and three parts.

• The whole game provides a high-level overview of sceptre. First-time users can get started by reading this chapter only.

• Part I (Chapter 1 — Chapter 6) describes in greater detail how to carry out a sceptre analysis on a laptop or desktop.

• Part II (Chapter 7 — Chapter 9) describes how to apply sceptre to analyze large-scale data on a cluster or cloud.

• Part III (Chapter 10 — ongoing) explicates the methods and algorithms that underlie sceptre.

Finally, the Appendix contains several sections, including a glossary and FAQs.

Acknowledgments

We gratefully acknowledge Kathryn Roeder, Xihong Lin, Xuran Wang, John Morris, Kaishu Mason, Ziang Niu, Yixuan Qiu, and Songcheng Dai for contributing to the research and development underlying sceptre and ondisc. We additionally thank the many sceptre users who have provided and continue to provide valuable feedback on the package. Finally, we acknowledge generous funding from Analytics at Wharton, NSF grants DMS 2113072 and DMS 2310654, and NIH grant R01MH123184.