转录基因数据的生物信息学分析 (批量和scRNA-Seq) 用于免疫瘤学
Mai Hanh Nguyen1,2,3, Nguyen Quoc Khanh Le4,5
1International Ph.D. Program in Cell Therapy and Regenerative Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Methods in molecular biology (Clifton, N.J.)
|September 30, 2025
概括
生物信息学对于分析来自下一代测序等方法的复杂免疫学数据至关重要. 它有助于解码免疫细胞多样性和基因表达,以更好地理解和治疗疾病.
科学领域:
- 免疫学和生物信息学
- 计算生物学在免疫研究中的应用
背景情况:
- 像下一代测序 (NGS) 这样的高通量技术在免疫学中产生了庞大的数据集.
- 了解免疫系统动态需要分析免疫细胞异质性,基因表达和受体谱.
研究的目的:
- 为免疫学中的生物信息学工具和技术提供全面指南.
- 为了促进免疫学研究的有效数据分析和解释.
主要方法:
- 对免疫基因签名进行转录组分析.
- 单细胞测序用于免疫细胞分析.
- 对T细胞和B细胞多样性的免疫谱系分析.
- 为了监管网络发现而集成多态经济学.
主要成果:
- 生物信息学能够深入了解免疫系统的动态.
- 免疫细胞异质性和基因表达模式的识别.
- 通过谱系分析了解T细胞和B细胞的多样性.
结论:
- 生物信息学对于推动免疫学研究至关重要.
- 这些方法有助于了解健康和疾病中的免疫反应.
- 生物信息学有助于识别治疗点和精确免疫疗法.
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