Principal network analysis

General Information

Description	Identification of subnetworks representing major dynamics using gene expression data
Homepage	http://sbm.postech.ac.kr/pna

Publications

Bioinformatics (2010)

Principal network analysis: identification of subnetworks representing major dynamics using gene expression data

Yongsoo Kim; Taek-Kyun Kim; Yungu Kim; Jiho Yoo; Sungyong You; Inyoul Lee; George Carlson; Leroy Hood; Seungjin Choi; Daehee Hwang

Motivation: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions.

Results: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection.

Availability: The web-based software is available at: http://sbm.postech.ac.kr/pna.

Contact: dhhwang@postech.ac.kr; seungjin@postech.ac.kr

Supplementary information: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btq670

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