Mapping Cell Identity from scRNA-seq: A primer on computational methods

Abstract

Single cell (sc) technologies mark a conceptual and methodological breakthrough in our way to study cells, the base units of life. Thanks to these technological developments, large-scale initiatives are currently ongoing aimed at mapping of all the cell types in the human body, with the ambitious aim to gain a cell-level resolution of physiological development and disease. Since its broad applicability and ease of interpretation scRNA-seq is probably the most common sc-based application. This assay uses high throughput RNA sequencing to capture gene expression profiles at the sc-level. Subsequently, under the assumption that differences in transcriptional programs correspond to distinct cellular identities, ad-hoc computational methods are used to infer cell types from gene expression patterns. A wide array of computational methods were developed for this task. However, depending on the underlying algorithmic approach and associated computational requirements, each method might have a specific range of application, with implications that are not always clear to the end user. Here we will provide a concise overview on state-of-the-art computational methods for cell identity annotation in scRNA-seq, tailored for new users and non-computational scientists. To this end, we classify existing tools in five main categories, and discuss their key strengths, limitations and range of application.

Keywords: Cell identity; Cell type annotation; RNAseq; ScRNAseq; Transcriptomics.

Fig. 1

Schematic representation of the five distinct main conceptual frameworks used for the implementation of computational methods for the classification of cell type identities from scRNA-seq data: A) marker-based (MB). Counts matrix of unlabelled cells (green table) are annotated through ad hoc scoring systems employing cell type specific lists of genes (indicated by different colours); B) classical machine learning (C-ML). Machine learning algorithms are trained with labelled (orange) counts matrix data (blue); C) semi-supervised learning (SSL). Labelled data (blue= count matrix and orange= annotation labels) and unlabeled data (green) processed in the same analytical workflow to transfer labels; D) deep learning (DL); a recent breakthrough in ML applies neural networks to learn labelled data (counts=blue, labels=orange) E) Hybrid methods any of A to D is combined in a single workflow.