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使用机器学习的转录组分析瘤微环境中的免疫细胞的协议
Yunxi Liao1, Ziyan Rao1, Shaodong Huang1
1Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China.
STAR protocols
|January 14, 2024
概括
这项研究介绍了一种使用单细胞RNA测序分析瘤微环境变化的协议. 它有助于识别免疫细胞透和癌症免疫治疗的潜在药物标.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
背景情况:
- 免疫疗法为癌症治疗提供了一个有希望的途径.
- 了解瘤微环境对于有效的癌症疗法至关重要.
- 现型变化和免疫细胞透显著影响治疗结果.
研究的目的:
- 提出分析瘤微环境中基于转录基因组的表型变化的详细方案.
- 研究免疫细胞透及其与癌症进展的关联.
- 为了确定癌症免疫治疗的潜在治疗点.
主要方法:
- 单细胞RNA测序 (scRNA-seq) 数据的整合.
- 单核细胞表型和起源的比较分析.
- 使用机器学习推断细胞分化轨迹和透过程.
- 识别与透相关的基因.
- 使用集成的微阵列和大量RNA-seq数据进行预测影响探索.
主要成果:
- 该协议使瘤免疫细胞透的全面分析成为可能.
- 机器学习识别了与免疫细胞透相关的关键基因.
- 分析揭示了基因表达和患者预后之间的潜在相关性.
- 已识别的基因作为新药开发的潜在目标.
结论:
- 提出的协议有助于深入分析瘤免疫微环境.
- 这种方法有助于识别癌症免疫治疗的预后生物标志物和潜在的治疗点.
- 这项研究为利用scRNA-seq数据在癌症研究中提供了一个框架.
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