Preface

The ability to profile genome-wide transcriptome at a single-cell level is become readily available, largely due to the availability of comercial kits. Single-cell RNA-sequencing (scRNA-seq) allows for the dissection of transcriptional events at the smallest, bioloically-relevant scale possible i.e. within single cells. The alluring promise to answer previously unsolvable problems and the ease of experimentation has led to an explosion of single-cell studies as well as dedicated computational tools.

However, much of the analysis of scRNA-seq is often applied in a black box manner. Thus, I have decided to write this guide, aiming to explain the rationale behind different analysis step. I hope that this guide can not only help others make more informed decisions during analysis but also serve as a reference when doing my own analysis.

Content-wise, the guide aims to cover the bioinformatics analysis of single-cell data from raw counts to making some biological interpretations through clustering analysis / differential gene expression and dimension reduction / trajectory inference. This will be broken down into several chapters and sections. And in each section, I will first explain the rationale behind performing the analysis, discuss some of the recent progress in terms of computational tools and benchmarking studies. This is finally followed by providing some code in R, with special attention on important arguments that needs to be provided in the functions.

If you find any errors or wish to offer any feedback, feel free to contact me at .

And let’s dive in!