Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes.
Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes.
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DOI：
10.1186/s12864-015-2170-4
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发表时间：
2015-11-11
期刊：
影响因子：
4.4
通讯作者：
Kaminski N
中科院分区：
文献类型：
--
作者：
Kim S;Herazo-Maya JD;Kang DD;Juan-Guardela BM;Tedrow J;Martinez FJ;Sciurba FC;Tseng GC;Kaminski N
在复杂疾病的研究中，关于仔细分型的患者的多组学信息的增加需要新的数据集成方法。与大多数组学数据集的连续强度测量不同，表现组数据包含二进制、有序和分类的临床变量。本文介绍了一种用于疾病亚型发现的综合表型框架(IPF)。为了对多组学数据进行有效降维和可视化，开发了特征拓扑图。该方法不需要模型假设，对数据噪声或缺失具有较强的鲁棒性。我们开发了一种工作流程，以聚集的方式集成来自不同组学数据的同质患者聚类，然后可视化成对组学来源的异质聚类。我们将该框架应用于两批来自诊断为慢性阻塞性肺疾病(COPD)或间质性肺病(ILD)的患者的肺样本，这些样本具有良好的临床(表型)数据、mRNA和microRNA表达谱。将IPF应用于第一批培训，确定了由同质疾病表型组成的患者群以及具有中间疾病特征的群。对第二批数据的分析显示了类似的数据结构，证实了中间星团的存在。中间簇中的基因富含炎症和免疫功能注释，表明它们代表了可能对免疫调节治疗有反应的机械上不同的疾病亚型。IPF软件包和所有源代码都是公开提供的。具有不同临床和生物分子特征的亚群的鉴定表明，表型和其他组学信息的整合可能导致新的基于机制的疾病亚型的鉴定。本文的在线版本(doi：10.1186/s12864-015-2170-4)包含补充材料，授权用户可以使用。
The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical. In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available. Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes. The online version of this article (doi:10.1186/s12864-015-2170-4) contains supplementary material, which is available to authorized users.