Interpreting Personal Transcriptomes: Personalized Mechanism-Scale Profiling of RNA-seq Data
Interpreting Personal Transcriptomes: Personalized Mechanism-Scale Profiling of RNA-seq Data
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解读个人转录组：RNA-seq 数据的个性化机制规模分析
DOI：
10.1142/9789814447973_0016
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发表时间：
2012
影响因子：
--
通讯作者：
Y. Lussier
中科院分区：
文献类型：
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作者：
Alan Perez;Haiquan Li;Y. Lussier
尽管有成千上万的研究报告揭示了复杂疾病的基因水平特征，但这些技术中很少有在单样本水平上工作，并明确支持生物学机制。这既给个性化医疗领域带来了严峻的困境，也为RNA-seq数据的分析提供了大量的机会。在这项研究中，我们假设我们开发的“个体微阵列表达的功能分析”（FAIME）方法可以顺利扩展到RNA-seq数据，并揭示同一复杂疾病不同规模生物学数据的内在潜在机制特征。使用公开的胃癌RNA-seq数据，我们证实了这种方法的有效性：（i）将每个样本转录组转化为通路量表评分，（ii）根据金标准预测胃癌中失调的通路（FDR<5%，精确度= 75%，召回率=92%），以及（iii）在独立的数据集和表达平台中预测表型（RNA-seq vs微阵列，Fisher精确检验p<10（-6））。在单样本水平上测量，FAIME可以区分癌症样本与正常样本;此外，与最先进的交叉样本方法相比，它在识别差异表达途径方面具有相当的性能。这些结果激发了未来在机制水平生物标志物发现方面的工作，这些生物标志物可预测诊断，治疗和治疗。
Despite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-seq data. In this study, we hypothesize that the "Functional Analysis of Individual Microarray Expression" (FAIME) method we developed could be smoothly extended to RNA-seq data and unveil intrinsic underlying mechanism signatures across different scales of biological data for the same complex disease. Using publicly available RNA-seq data for gastric cancer, we confirmed the effectiveness of this method (i) to translate each sample transcriptome to pathway-scale scores, (ii) to predict deregulated pathways in gastric cancer against gold standards (FDR<5%, Precision=75%, Recall =92%), and (iii) to predict phenotypes in an independent dataset and expression platform (RNA-seq vs microarrays, Fisher Exact Test p<10(-6)). Measuring at a single-sample level, FAIME could differentiate cancer samples from normal ones; furthermore, it achieved comparative performance in identifying differentially expressed pathways as compared to state-of-the-art cross-sample methods. These results motivate future work on mechanism-level biomarker discovery predictive of diagnoses, treatment, and therapy.
影响因子：
11.2
作者：
Kim YH;Liang H;Liu X;Lee JS;Cho JY;Cheong JH;Kim H;Li M;Downey TJ;Dyer MD;Sun Y;Sun J;Beasley EM;Chung HC;Noh SH;Weinstein JN;Liu CG;Powis G
通讯作者：
Powis G
走向机制分类器：表达锚定的基因本体特征可以预测肺腺癌患者的临床结果。
DOI：
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发表时间：
2012
期刊：
AMIA ... Annual Symposium proceedings. AMIA Symposium
影响因子：
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作者：
Yang,Xinan;Li,Haiquan;Regan,Kelly;Li,Jianrong;Huang,Yong;Lussier,YvesA
通讯作者：
Lussier,YvesA