SingleR Sticker

Imagine a world without a reference genome. Whenever we receive new RNA-seq data, we’d need to run it through an assembler to identify the expressed sequences. We would then need to inspect each sequence to determine its likely function, e.g., based on sequence motifs. This process is analogous to current practice in single-cell data analysis; simply replace reads with cells, assemblies with clusters, and genes with cell types. A typical practitioner will hope that their clusters are reasonable proxies for the biological states of interest and that their manual annotation of the clusters is accurate. Such an “artisanal” process is difficult to reproduce and scale to larger datasets involving more diverse cell types.

The solution is to perform automated cell type annotation, a.k.a. cell type classification (or occasionally, “label transfer”). These methods compare cells in a new dataset against curated reference profiles of known cell types, assigning each new cell to the reference type that its expression profile is most similar to. This allows users to skip the mundane annotation of their data and jump directly to the interesting questions - does my cell type change in abundance or expression across treatments? Is there interesting substructure within an existing population? In this respect, automated annotation methods are the single-cell field’s equivalent to genome aligners, and we anticipate that the former will also become standard procedure for single-cell data analysis.

This book covers the use of SingleR, one implementation of an automated annotation method. If you want a survey of different annotation methods - this book is not for you. If you want to create hand-crafted cluster definitions - this book is not for you. (Read the other one instead.) If you want to use the pre-Bioconductor version of the package - this book is not for you. But if you’re tired of manually annotating your single-cell data and you want to do something better with your life, then read on.