结直肠癌（CRC）是最常见的胃肠恶性肿瘤之一，但对于CRC患者的复发风险标志很少。单细胞RNA测序（scRNA-seq）为预测标志检测提供了高分辨率的平台，但由于成本高，大多数单细胞实验缺乏临床表型信息，因此在大型队列中不实用。少数研究报道使用带有生存时间的外部批量转录组指导scRNA-seq数据中关键细胞亚种的检测。我们提出了scRankXMBD，这是一个基于单细胞转录组中基因对的同一细胞相对表达顺序，优先考虑与预后相关的细胞亚群的计算框架。与五种现有方法相比，scRankXMBD具有更高的准确度和一致性。此外，我们开发了单细胞基因对标志，可预测患者的复发风险。我们的工作促进了基于秩的方法在scRNA-seq数据中预测生物标记物的发现和精准肿瘤学的应用。scRankXMBD可在https://github.com/xmuyulab/scRank-XMBD获得（XMBD：厦门大数据，中国厦门大学国家数据科学健康医学研究所的生物医学开放软件计划）。 ©作者2023年。由牛津大学出版。保留所有权利。如需获得许可，请发送电子邮件至：journals.permissions@oup.com。

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high-resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq data. We proposed scRankXMBD, a computational framework to prioritize prognostic-associated cell subpopulations based on within-cell relative expression orderings of gene pairs from single-cell transcriptomes. scRankXMBD achieves higher precision and concordance compared with five existing methods. Moreover, we developed single-cell gene pair signatures to predict recurrence risk for patients individually. Our work facilitates the application of the rank-based method in scRNA-seq data for prognostic biomarker discovery and precision oncology. scRankXMBD is available at https://github.com/xmuyulab/scRank-XMBD. (XMBD:Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.).© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.