使用单细胞基因组和转录组测序数据共同推断克隆结构
Xiangqi Bai1, Zhana Duren2, Lin Wan3,4
1Division of Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
NAR genomics and bioinformatics
|March 15, 2024
概括
一个新的计算框架,CCNMF,集成单细胞基因组 (scDNA) 和转录组 (scRNA) 数据来识别细胞克隆. 这种方法将副本数和基因表达联系起来,揭示复杂的生物样本中的克隆结构.
科学领域:
- 基因组学就是基因组学.
- 文字转录学 (Transcriptomics) 是一个学科.
- 计算生物学 计算生物学
背景情况:
- 高通量单细胞测序 (scDNA和scRNA) 允许细胞解析组织克隆研究.
- 整合来自同一样本的异质scRNA和scDNA数据用于克隆分析仍然是一个挑战.
研究的目的:
- 从匹配的scDNA和scRNA数据中共同推断克隆结构的计算框架 (CCNMF).
- 通过将副本号码和基因表达特征联系起来,将多个omics单细胞结合起来.
主要方法:
- 开发了结合式克隆非负矩阵因数分解 (CCNMF).
- CCNMF将scDNA拷贝数的改变与scRNA基因表达特征联系起来.
- 使用模拟数据和真实世界癌症样本 (卵巢,胃) 进行验证.
主要成果:
- 通过关联克隆基因组和转录组,CCNMF成功地解决了共存的克隆.
- 在模拟和现实世界数据集中展示了高准确性和稳定性.
- 启用了基因组和转录基因组克隆架构的同时解析.
结论:
- CCNMF是一个强大的工具,用于整合多omics单细胞数据.
- 促进对基因表达变化以及克隆基因组改变的理解.
- 阐明了子克隆基因组差异对瘤进化有所贡献.
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