Local network component analysis for quantifying transcription factor activities

doi:10.1016/j.ymeth.2017.06.018

	Local network component analysis for quantifying transcription factor activities
	Shi, Qianqian 1; Zhang, Chuanchao 3; Guo, Weifeng 4; Zeng, Tao 1; Lu, Lina 1; Jiang, Zhonglin 1; Wang, Ziming1,2 ; Liu, Juan 3; Chen, Luonan1,2
	2017-07-15
发表期刊	METHODS (IF:4.2[JCR-2023],3.8[5-Year])
ISSN	1046-2023
卷号	124 页码:25-35
发表状态	已发表
DOI	10.1016/j.ymeth.2017.06.018
摘要	Transcription factors (TFs) could regulate physiological transitions or determine stable phenotypic diversity. The accurate estimation on TF regulatory signals or functional activities is of great significance to guide biological experiments or elucidate molecular mechanisms, but still remains challenging. Traditional methods identify TF regulatory signals at the population level, which masks heterogeneous regulation mechanisms in individuals or subgroups, thus resulting in inaccurate analyses. Here, we propose a novel computational framework, namely local network component analysis (LNCA), to exploit data heterogeneity and automatically quantify accurate transcription factor activity (TFA) in practical terms, through integrating the partitioned expression sets (i.e., local information) and prior TF-gene regulatory knowledge. Specifically, LNCA adopts an adaptive optimization strategy, which evaluates the local similarities of regulation controls and corrects biases during data integration, to construct the TFA landscape. In particular, we first numerically demonstrate the effectiveness of LNCA for the simulated data sets, compared with traditional methods, such as FastNCA, ROBNCA and NINCA. Then, we apply our model to two real data sets with implicit temporal or spatial regulation variations. The results show that LNCA not only recognizes the periodic mode along the S. cerevisiae cell cycle process, but also substantially outperforms over other methods in terms of accuracy and consistency. In addition, the cross validation study for glioblastomas multiforme (GBM) indicates that the TFAs, identified by LNCA, can better distinguish clinically distinct tumor groups than the expression values of the corresponding TFs, thus opening a new way to classify tumor subtypes and also providing a novel insight into cancer heterogeneity. (C) 2017 Elsevier Inc. All rights reserved.
关键词	Network component analysis Integrative analysis Data heterogeneity Transcription factor activities Adaptive optimization strategy
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of Shanghai[17ZR1446100]
WOS研究方向	Biochemistry & Molecular Biology
WOS类目	Biochemical Research Methods ; Biochemistry & Molecular Biology
WOS记录号	WOS:000407409000004
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS关键词	NONLINEAR DIMENSIONALITY REDUCTION ; REGULATORY NETWORKS ; DISEASE ; IDENTIFICATION ; PROGRESSION ; PREDICTION ; INITIATION ; FRAMEWORK ; PHASE ; CHIP
原始文献类型	Article
通讯作者	Chen, Luonan
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2894
专题	生命科学与技术学院生命科学与技术学院_特聘教授组_陈洛南组生命科学与技术学院_硕士生
通讯作者	Chen, Luonan
作者单位	1.Univ Chinese Acad Sci, Chinese Acad Sci, Shanghai Inst Biol Sci,Key Lab Syst Biol, Inst Biochem & Cell Biol,CAS Ctr Excellence Mol C, Shanghai, Peoples R China 2.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China 3.Wuhan Univ, Sch Comp, State Key Lab Software Engn, Wuhan, Hubei, Peoples R China 4.Northwestern Polytech Univ, Sch Automat, Minist Educ, Key Lab Informat Fus Technol, Xian, Shaanxi, Peoples R China
通讯作者单位	生命科学与技术学院
推荐引用方式 GB/T 7714	Shi, Qianqian,Zhang, Chuanchao,Guo, Weifeng,et al. Local network component analysis for quantifying transcription factor activities[J]. METHODS,2017,124:25-35.
APA	Shi, Qianqian.,Zhang, Chuanchao.,Guo, Weifeng.,Zeng, Tao.,Lu, Lina.,...&Chen, Luonan.(2017).Local network component analysis for quantifying transcription factor activities.METHODS,124,25-35.
MLA	Shi, Qianqian,et al."Local network component analysis for quantifying transcription factor activities".METHODS 124(2017):25-35.