Department of Integrative Biology & Pharmacology, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Sage Bionetworks, Seattle, WA 98109, USA.
Brief Bioinform. 2021 Sep 2;22(5). doi: 10.1093/bib/bbab039.
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.
单细胞 RNA 测序 (scRNA-Seq) 是一种新兴的免疫细胞群体特征分析策略。与流式细胞术或质谱流式细胞术相比，scRNA-Seq 有可能鉴定缺乏精确细胞表面标志物的细胞类型和激活状态。然而，由于需要从转录谱手动对每个免疫细胞进行分类，scRNA-Seq 目前受到限制。虽然最近开发的算法可以准确注释粗细胞类型（例如 T 细胞与巨噬细胞），但要进行精细区分（例如 CD8+效应记忆 T 细胞）仍然是一个具有挑战性的难题。为了解决这个问题，我们开发了一种名为 ImmClassifier 的机器学习分类器，该分类器利用了细胞类型的层次本体。我们证明，它的预测与 CITE-seq 的基于流式的标志物高度一致，并且在区分精细的细胞类型方面优于其他工具（召回率提高 15%，精度提高 14%），而在粗细胞类型上具有可比的性能。因此，ImmClassifier 可用于更深入地探索 scRNA-Seq 实验中免疫系统的异质性。
