UBD:从批量样本中结合细胞类型比例估计的不确定性,以推断细胞类型特定的个人资料
Youshu Cheng1,2, Chen Lin1, Hongyu Li1
1Department of Biostatistics, Yale School of Public Health, 47 College St, New Haven, CT 06510, United States.
Briefings in bioinformatics
|January 11, 2026
概括
本研究引入了不确定性意识的贝叶斯解卷 (UBD) 方法,通过考虑到细胞类型比例的不确定性,从散装组织数据中改进细胞类型特异性 (CTS) 概况估计. UBD将这些比例改进,并同时估计CTS数据,提高准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 统计解卷方法从大量组织数据中估计细胞类型特异性 (CTS) 配置文件.
- 现有的方法需要已知的细胞类型比例,当估计被用作基本事实时引入不确定性.
研究的目的:
- 开发一种新的方法,不确定性意识贝叶斯解卷 (UBD),该方法将不确定性纳入细胞类型比例估计.
- 精制细胞类型比例,同时估计样本级CTS数据.
主要方法:
- UBD使用贝叶斯解卷法来明确地模拟初始细胞类型比例估计中的不确定性.
- 该方法完善了这些比例,并同时估计了CTS配置文件.
主要成果:
- 广泛的模拟表明,UBD显著提高了CTS概况估计的准确性.
- 在应用到两个真实生物数据集时,UBD成功识别了额外的CTS信号.
结论:
- 通过处理细胞类型比例的不确定性,UBD提供了一种强大的方法来解决现有解卷方法的局限性.
- 这种方法提高了从散装组织数据中获得的CTS配置文件的可靠性和发现潜力.
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