SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks

Abstract The establishment of a collaborative network of transcription factors (TFs) followed by decomposition and then construction of subnetworks is an effective way to obtain sets of collaborative TFs; each set controls a biological process or a complex trait. We previously developed eight gene a...

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Autores principales:	Xiaohui Ji, Su Chen, Jun Cheng Li, Wenping Deng, Zhigang Wei, Hairong Wei
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3c5b79222bb04350a813b7e3b0ad5184
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spelling	oai:doaj.org-article:3c5b79222bb04350a813b7e3b0ad51842021-12-02T16:06:39ZSSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks10.1038/s41598-017-01556-z2045-2322https://doaj.org/article/3c5b79222bb04350a813b7e3b0ad51842017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01556-zhttps://doaj.org/toc/2045-2322Abstract The establishment of a collaborative network of transcription factors (TFs) followed by decomposition and then construction of subnetworks is an effective way to obtain sets of collaborative TFs; each set controls a biological process or a complex trait. We previously developed eight gene association methods for genome-wide coexpression analysis between each TF and all other genomic genes and then constructing collaborative networks of TFs but only one algorithm, called Triple-Link Algorithm, for building collaborative subnetworks. In this study, we developed two more algorithms, Single Seed-Growing Algorithm (SSGA) and Multi-Seed Growing Algorithm (MSGA), for building collaborative subnetworks of TFs by identifying the fully-linked triple-node seeds from a decomposed collaborative network and then growing them into subnetworks with two different strategies. The subnetworks built from the three algorithms described above were comparatively appraised in terms of both functional cohesion and intra-subnetwork association strengths versus inter-subnetwork association strengths. We concluded that SSGA and MSGA, which performed more systemic comparisons and analyses of edge weights and network connectivity during subnetwork construction processes, yielded more functional and cohesive subnetworks than Triple-Link Algorithm. Together, these three algorithms provide alternate approaches for acquiring subnetworks of collaborative TFs. We also presented a framework to outline how to use these three algorithms to obtain collaborative TF sets governing biological processes or complex traits.Xiaohui JiSu ChenJun Cheng LiWenping DengZhigang WeiHairong WeiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaohui Ji Su Chen Jun Cheng Li Wenping Deng Zhigang Wei Hairong Wei SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks
description	Abstract The establishment of a collaborative network of transcription factors (TFs) followed by decomposition and then construction of subnetworks is an effective way to obtain sets of collaborative TFs; each set controls a biological process or a complex trait. We previously developed eight gene association methods for genome-wide coexpression analysis between each TF and all other genomic genes and then constructing collaborative networks of TFs but only one algorithm, called Triple-Link Algorithm, for building collaborative subnetworks. In this study, we developed two more algorithms, Single Seed-Growing Algorithm (SSGA) and Multi-Seed Growing Algorithm (MSGA), for building collaborative subnetworks of TFs by identifying the fully-linked triple-node seeds from a decomposed collaborative network and then growing them into subnetworks with two different strategies. The subnetworks built from the three algorithms described above were comparatively appraised in terms of both functional cohesion and intra-subnetwork association strengths versus inter-subnetwork association strengths. We concluded that SSGA and MSGA, which performed more systemic comparisons and analyses of edge weights and network connectivity during subnetwork construction processes, yielded more functional and cohesive subnetworks than Triple-Link Algorithm. Together, these three algorithms provide alternate approaches for acquiring subnetworks of collaborative TFs. We also presented a framework to outline how to use these three algorithms to obtain collaborative TF sets governing biological processes or complex traits.
format	article
author	Xiaohui Ji Su Chen Jun Cheng Li Wenping Deng Zhigang Wei Hairong Wei
author_facet	Xiaohui Ji Su Chen Jun Cheng Li Wenping Deng Zhigang Wei Hairong Wei
author_sort	Xiaohui Ji
title	SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks
title_short	SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks
title_full	SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks
title_fullStr	SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks
title_full_unstemmed	SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks
title_sort	ssga and msga: two seed-growing algorithms for constructing collaborative subnetworks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/3c5b79222bb04350a813b7e3b0ad5184
work_keys_str_mv	AT xiaohuiji ssgaandmsgatwoseedgrowingalgorithmsforconstructingcollaborativesubnetworks AT suchen ssgaandmsgatwoseedgrowingalgorithmsforconstructingcollaborativesubnetworks AT junchengli ssgaandmsgatwoseedgrowingalgorithmsforconstructingcollaborativesubnetworks AT wenpingdeng ssgaandmsgatwoseedgrowingalgorithmsforconstructingcollaborativesubnetworks AT zhigangwei ssgaandmsgatwoseedgrowingalgorithmsforconstructingcollaborativesubnetworks AT hairongwei ssgaandmsgatwoseedgrowingalgorithmsforconstructingcollaborativesubnetworks
_version_	1718384897530265600

SSGA and MSGA: two seed-growing algorithms for constructing collaborative subnetworks

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