rPAC: Route based pathway analysis for cohorts of gene expression data sets.
rPAC: Route based pathway analysis for cohorts of gene expression data sets.
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DOI：
10.1016/j.ymeth.2021.10.002
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Shin DG
中科院分区：
文献类型：
--
作者：
Joshi P;Basso B;Wang H;Hong SH;Giardina C;Shin DG
通路分析是一种流行的方法，旨在从高通量基因表达研究中获得生物学解释。然而，现有的方法主要集中在确定哪些途径或途径可能已经受到干扰，给定差异基因表达模式。在本文中，我们提出了一种新的途径分析框架，即rPAC，它分解成两个部分，上游部分的转录因子（TF）块和下游部分的TF块，并产生一个通路路径扰动分析方案检查分配给这两个部分的干扰分数在一起。该rPAC评分进一步应用于基因表达数据集的群组，其产生两个汇总度量，“显著性比例”（PS）和“平均途径评分”（ARS），作为辨别群组内和/或群组之间的扰动途径途径的定量测量。为了证明rPAC的评分能力，我们首先使用了大量的模拟数据，并比较了该方法的性能与传统方法的功率曲线。接下来，我们进行了一个案例研究，涉及来自癌症基因组图谱（TCGA）的三个上皮癌数据集。rPAC方法揭示了作为潜在癌症类型特征的特定途径。更深入的亚组路径分析（即，COAD中的年龄组或BRCA中的癌症亚型）导致已知与亚组相关的途径途径。此外，还发现了多个先前未表征的途径，这可能表明rPAC在解读疾病病因方面优于传统方法，特别是在以更细的粒度分离受干扰途径的途径和部分方面。
Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. However, existing methods focus mostly on identifying which pathway or pathways could have been perturbed, given differential gene expression patterns. In this paper, we present a novel pathway analysis framework, namely rPAC, which decomposes each signaling pathway route into two parts, the upstream portion of a transcription factor (TF) block and the downstream portion from the TF block and generates a pathway route perturbation analysis scheme examining disturbance scores assigned to both parts together. This rPAC scoring is further applied to a cohort of gene expression data sets which produces two summary metrics, “Proportion of Significance” (PS) and “Average Route Score” (ARS), as quantitative measures discerning perturbed pathway routes within and/or between cohorts. To demonstrate rPAC’s scoring competency, we first used a large amount of simulated data and compared the method’s performance against those by conventional methods in terms of power curve. Next, we performed a case study involving three epithelial cancer data sets from The Cancer Genome Atlas (TCGA). The rPAC method revealed specific pathway routes as potential cancer type signatures. A deeper pathway analysis of sub-groups (i.e., age groups in COAD or cancer sub-types in BRCA) resulted in pathway routes that are known to be associated with the sub-groups. In addition, multiple previously uncharacterized pathways routes were identified, potentially suggesting that rPAC is better in deciphering etiology of a disease than conventional methods particularly in isolating routes and sections of perturbed pathways in a finer granularity.
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