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Differential function analysis: identifying structure and activation variations in dysregulated pathways


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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, DFA is a general framework to capture the systematical changes of genes, networks and functions of complex diseases, which not only provides the new insight on the simultaneous alterations of pathway genes and pathway activations, but also opens a new way for the network-based functional analysis on heterogeneous diseases.


复杂疾病通常由生物功能, 而不是单个分子的失调造成的。因此, 系统性地研究复杂疾病的主要挑战是如何捕捉差异调节的生物功能。传统的差异表达分析(DEA), 通常考虑基因, 而不是功能的改变的表达值。同时, 传统的基于功能的分析(例如, PEA:功能途径富集分析)主要考虑功能活性的变化, 而忽略了功能内遗传基因之间的结构变化。在这个工作中, 我们开发了一个新的差分功能分析(DFA)算法, 它能够同时识别遗传基因之间的结构和功能活性的变化。为了验证我们方法的有效性, 我们在各种数据集上测试DFA, 结果表明DFA是能够有效地还原功能内遗传基因之间的结构变化和功能活性的失调。总之, DFA提供了一个系统性地窥视复杂疾病的功能, 网络, 基因变化的工具。

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Correspondence to Juan Liu or Tao Zeng or Luonan Chen.

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Zhang, C., Liu, J., Shi, Q. et al. Differential function analysis: identifying structure and activation variations in dysregulated pathways. Sci. China Inf. Sci. 60, 012108 (2017). https://doi.org/10.1007/s11432-016-0030-6

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  • complex disease
  • biological function
  • non-negative matrix factorization
  • network structure
  • function activation


  • 复杂疾病
  • 生物功能
  • 非负矩阵分解
  • 网络结构
  • 功能活性