Differential function analysis: identifying structure and activation variations in dysregulated pathways
Differential function analysis: identifying structure and activation variations in dysregulated pathways
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差异功能分析：识别失调途径的结构和激活变化
DOI：
10.1007/s11432-016-0030-6
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Chen Luonan
中科院分区：
文献类型：
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作者：
Zhang Chuanchao;Liu Juan;Shi Qianqian;Zeng Tao;Chen Luonan
复杂疾病通常是由生物功能失调而不是单个分子引起的。因此，对复杂疾病进行系统研究的一个主要挑战是如何捕捉差异调节的生物功能，例如途径。传统的差异表达分析（DEA）通常考虑基因表达值的变化，而不是功能的变化。同时，传统的基于功能的分析（如PEA: pathway enrichment analysis）主要考虑功能激活的变化，而忽略了功能遗传元件的结构变化。为了实现针对复杂疾病的精准医疗，有必要将疾病发生发展过程中功能及其要素的变化与异质失调途径区分开来。在这项工作中，与传统的DEA相比，我们基于比较非负矩阵分解（cNMF）开发了一种新的计算框架，即差分功能分析（DFA），以识别生物功能的元件结构变化和表达激活。为了验证该方法的有效性，我们在不同的数据集上进行了DFA测试，结果表明DFA能够有效地恢复预先设置的功能基团的差异元素结构和差异激活分数。特别是，对人类胃癌数据集的DFA分析，不仅捕获了与胃癌相关的通路网络结构的变化，而且检测了这些通路的差异激活（即显著区分正常样本和疾病样本），这比目前最先进的方法，如GSVA和Pathifier更有效。完全,
Complex diseases are generally caused by the dysregulation of biological functions rather than individual molecules. Hence, a major challenge of the systematical study on complex diseases is how to capture the differentially regulated biological functions, e.g., pathways. The traditional differential expression analysis (DEA) usually considers the changed expression values of genes rather than functions. Meanwhile, the conventional function-based analysis (e.g., PEA: pathway enrichment analysis) mainly considers the varying activation of functions but disregards the structure change of genetic elements of functions. To achieve precision medicine against complex diseases, it is necessary to distinguish both the changes of functions and their elements from heterogeneous dysregulated pathways during the disease development and progression. In this work, in contrast to the traditional DEA, we developed a new computational framework, namely differential function analysis (DFA), to identify the changes of element-structure and expression-activation of biological functions, based on comparative non-negative matrix factorization (cNMF). To validate the effectiveness of our method, we tested DFA on various datasets, which shows that DFA is able to effectively recover the differential element-structure and differential activation-score of pre-set functional groups. In particular, the analysis of DFA on human gastric cancer dataset, not only capture the changed network-structure of pathways associated with gastric cancer, but also detect the differential activations of these pathways (i.e., significantly discriminating normal samples and disease samples), which is more effective than the state-of-the-art methods, such as GSVA and Pathifier. Totally,