Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data. Issue 5 (3rd March 2021)

• Record Type: Journal Article
• Authors: Liu, Xuan; Gosline, Sara J C; Pflieger, Lance T; Wallet, Pierre; Iyer, Archana; Guinney, Justin; Bild, Andrea H; Chang, Jeffrey T
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 5(2021)
• Publication Date: 2021-03-03
• Subjects: deep learning -- immune cell classification -- machine learning -- single-cell RNA-Seq; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab039
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.