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 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.

Regulation of regeneration in Arabidopsis thaliana

“…For example, PLT1-3 and BBM are involved in early embryo development (ten Hove et al 2015), LBD16 (Xu et al 2018b), and LBD29 (Xu et al 2018a) are involved in callus formation, indicating the high accuracy of the CollaborativeNet and TGMI algorithm. Which are consistent with a wealth of evidence we showed in our previous publications (Gunasekara et al 2018;Ji et al 2017;Nie et al 2011). One of the main strengths of our study lies in the integration of biological evaluation throughout each stage of the procedure.…”

“…In our study, we employed a method named Collabo-rativeNet (https://github.com/hwei0805/TF_ Collabo-rativeNet) (Ji et al 2017;Nie et al 2011), initially known as TF-Cluster, to build a collaborative network of all TFs, which was subsequently decomposed into collaborative subnetworks using the Triple-Link algorithm integrated within CollaborativeNet package. Each subnetwork was proven to regulate a biological process as evidenced by the examples shown in original publications and some other subsequent studies.…”