1 Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA.
2 Center for Biomedical Informatics and Biostatistics (CB2), The University of Arizona, Tucson, AZ, USA.
Stat Methods Med Res. 2018 Dec;27(12):3797-3813. doi: 10.1177/0962280217712271. Epub 2017 May 29.
Modern precision medicine increasingly relies on molecular data analytics, wherein development of interpretable single-subject ("N-of-1") signals is a challenging goal. A previously developed global framework, N-of-1- pathways, employs single-subject gene expression data to identify differentially expressed gene set pathways in an individual patient. Unfortunately, the limited amount of data within the single-subject, N-of-1 setting makes construction of suitable statistical inferences for identifying differentially expressed gene set pathways difficult, especially when non-trivial inter-gene correlation is present. We propose a method that exploits external information on gene expression correlations to cluster positively co-expressed genes within pathways, then assesses differential expression across the clusters within a pathway. A simulation study illustrates that the cluster-based approach exhibits satisfactory false-positive error control and reasonable power to detect differentially expressed gene set pathways. An example with a single N-of-1 patient's triple negative breast cancer data illustrates use of the methodology.
现代精准医学越来越依赖分子数据分析，其中开发可解释的单个体（“N-of-1”）信号是一个具有挑战性的目标。先前开发的全局框架“N-of-1 途径”利用单个体基因表达数据来识别个体患者中差异表达的基因集途径。不幸的是，在单个体 N-of-1 环境中，数据量有限，使得为识别差异表达的基因集途径构建合适的统计推断变得困难，尤其是在存在非平凡的基因间相关性时。我们提出了一种利用基因表达相关性的外部信息来对途径内正共表达基因进行聚类的方法，然后评估途径内各聚类的差异表达。一项模拟研究表明，基于聚类的方法具有令人满意的假阳性错误控制和合理的检测差异表达基因集途径的能力。一个使用单个 N-of-1 患者三阴性乳腺癌数据的示例说明了该方法的应用。
