Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.
PLoS Comput Biol. 2024 Jan 11;20(1):e1011717. doi: 10.1371/journal.pcbi.1011717. eCollection 2024 Jan.
We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods.
我们描述了一种新颖的用于癌症转录组学数据的单样本基因集测试方法,名为组织调整的癌症途径分析(TPAC)。TPAC 方法利用了人类基因的正常组织特异性信息来计算稳健的多变量距离得分,该得分量化了每个所分析肿瘤中的基因集失调。由于 TPAC 分数的零分布具有准确的伽马逼近,因此支持群体和样本水平的推断。正如我们通过对来自癌症基因组图谱(TCGA)的 21 种实体人类癌症的基因表达数据和来自人类蛋白质图谱(HPA)的相关正常组织表达数据进行分析所证明的那样,TPAC 基因集分数与患者预后的相关性比现有单样本基因集测试方法生成的分数更强。