Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbac002.
The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.
单细胞 RNA 测序 (scRNA-seq) 技术的进步使我们能够在细胞水平上描述转录组图谱，并在批量样本分析中展现出巨大的潜力，从而为将基因特征从单细胞 RNA-seq 转移到批量数据提供了机会。然而，由于不同测序技术的分析差异，从单细胞中鉴定的基因表达特征通常不适用于批量 RNA-seq 数据。在这里，我们提出了单细胞成对基因表达 (scPAGE)，这是一种开发对批量 RNA-seq 分类有益的单细胞基因对特征 (scGPS) 的新方法，以实现跨平台的知识转移。PAGE 被用于解决分析差异的挑战。我们将该方法应用于急性髓系白血病 (AML)，并从小鼠 scRNA-seq 中鉴定出 scGPS，可用于区分 AML 和对照细胞。scGPS 在批量 RNA-seq 数据集上得到验证，其性能优于传统的基因表达策略 (平均 AUC$\le$0.88) (平均 AUC=0.96)，表明其在揭示 AML 的分子机制方面具有潜力。scGPS 也优于其批量对应物，这突出了基因特征转移的优势。此外，我们还以脓毒症为例证实了 scPAGE 在其他疾病情况下的实用性。scPAGE 利用单细胞图谱的优势来增强批量样本的分析，从而显示出从单细胞到批量转录组研究转移知识的巨大潜力。
