| 引用本文: | 张凌瑞,胡龙飞,黄万翔,孙啸,范珏.基于单细胞转录组数据的疾病表型预测研究进展[J].生物信息学,2025,23(2):81-87. |
| ZHANG Lingrui,HU Longfei,HUANG Wanxiang,SUN Xiao,FAN Jue.Advances in the prediction of disease phenotypes usingsingle-cell transcriptomic data[J].Chinese Journal of Bioinformatics,2025,23(2):81-87. |
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| 摘要: |
| 单细胞转录组测序(scRNA-seq)已被广泛应用于基础医学研究中,分析和挖掘scRNA-seq数据有助于深入理解病变组织的细胞组成结构和功能,揭示复杂疾病过程和阐明药物作用机制,进而推动精准医学的发展。然而,如何基于海量的scRNA-seq数据对患者疾病表型进行预测,并筛选关键特征是单细胞技术临床转化的关键问题。本文综述了基于单细胞转录组数据进行患者疾病表型预测的相关方法,并对原理、算法、优缺点进行归纳和讨论,最后对相关研究的发展和应用进行了展望。 |
| 关键词: 单细胞转录组测序 疾病表型预测 机器学习 特征筛选 |
| DOI：10.12113/202403005 |
| 分类号:Q291 |
| 文献标识码:A |
| 基金项目: |
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| Advances in the prediction of disease phenotypes usingsingle-cell transcriptomic data |
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ZHANG Lingrui1,2, HU Longfei2, HUANG Wanxiang2, SUN Xiao1, FAN Jue2
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(1. School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 211189, China;2.Singleron BiotechCo., Ltd, Nanjing 210061, China)
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| Abstract: |
| Single-cell RNA sequencing (scRNA-seq) has been widely applied in basic medical research. Analyzing and mining scRNA-seq data facilitates an in-depth understanding of the cellular composition and function of diseased tissues, reveals complex disease processes, elucidates drug mechanisms of action, and promotes the development of precision medicine. However, how to predict patient disease phenotypes based on the massive amounts of scRNA-seq data and identify key features is a critical issue for the clinical translation of single-cell technologies. This article reviews relevant methods for predicting patient disease phenotypes using single-cell transcriptomic data, and summarizes their principles, algorithms, advantages and disadvantages. Finally, recommendations and perspectives on the application of related research are provided. |
| Key words: Single-cell transcriptome sequencing Disease phenotype prediction Machine learning Feature selection |
