Differential function analysis: identifying structure and activation variations in dysregulated pathways
文献类型：期刊论文
| 作者 | Chen Luonan1; Zeng Tao1; Shi Qianqian1; Zhang Chuanchao1; Liu Juan2; Zhang Chuanchao2 |
| 刊名 | Science China. Information Science
 |
| 出版日期 | 2017 |
| 卷号 | 60期号:1 |
| ISSN号 | 1674-733X |
| 关键词 | NONNEGATIVE MATRIX FACTORIZATION EARLY-WARNING SIGNALS COMPLEX DISEASES MICROARRAY DATA GASTRIC-CANCER MODULE NETWORK PREDICTION DISCOVERY ONTOLOGY NOISE complex disease biological function non-negative matrix factorization network structure function activation |
| DOI | 10.1007/s11432-016-0030-6 |
| 英文摘要 | 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. |
| 资助项目 | [Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)] ; [National Natural Science Foundation of China] ; [JSPS KAKENHI] |
| 语种 | 英语 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/285340]  |
| 专题 | 中国科学院上海药物研究所 |
| 作者单位 | 1.中国科学院上海药物研究所 2.Wuhan Univ Sch Comp State Key Lab Software Engn Wuhan 430072 Peoples R China; |
| 推荐引用方式 GB/T 7714 | Chen Luonan,Zeng Tao,Shi Qianqian,et al. Differential function analysis: identifying structure and activation variations in dysregulated pathways[J]. Science China. Information Science,2017,60(1). |
| APA | Chen Luonan,Zeng Tao,Shi Qianqian,Zhang Chuanchao,Liu Juan,&Zhang Chuanchao.(2017).Differential function analysis: identifying structure and activation variations in dysregulated pathways.Science China. Information Science,60(1). |
| MLA | Chen Luonan,et al."Differential function analysis: identifying structure and activation variations in dysregulated pathways".Science China. Information Science 60.1(2017). |
入库方式： OAI收割
来源：上海药物研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。