A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions.

Autor: Nock R; NICTA/Data61 & the Australian National University, Alexandria, Australia., Polouliakh N; Sony Computer Science Laboratories Inc., Tokyo, Japan.; Department of Ophthalmology and Visual Science, Yokohama City University, Yokohama, Japan.; Systems Biology Institute, Tokyo, Japan., Nielsen F; Sony Computer Science Laboratories Inc., Tokyo, Japan., Oka K; The University of Tokyo, Tokyo, Japan., Connell CR; Department of Computer Science, Queensland University of Technology, Brisbane, Australia., Heimhofer C; Department of Computer Science, ETH Zurich, Zurich, Switzerland., Shibanai K; Department of Computer Science, School of Computing, Tokyo Institute of Technology, Tokyo, Japan., Ghosh S; Systems Biology Institute, Tokyo, Japan., Aisaki KI; National Institute of Health Science, Kawasaki, Japan., Kitajima S; National Institute of Health Science, Kawasaki, Japan., Kanno J; Japan Bioassay Research Center, Japan Organization of Occupational Health and Safety, Hadano, Japan., Akama T; Sony Computer Science Laboratories Inc., Tokyo, Japan., Kitano H; Sony Computer Science Laboratories Inc., Tokyo, Japan.; Systems Biology Institute, Tokyo, Japan.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2020 Jul 06; Vol. 15 (7), pp. e0233755. Date of Electronic Publication: 2020 Jul 06 (Print Publication: 2020).
DOI: 10.1371/journal.pone.0233755
Abstrakt: Systems biology aims at holistically understanding the complexity of biological systems. In particular, nowadays with the broad availability of gene expression measurements, systems biology challenges the deciphering of the genetic cell machinery from them. In order to help researchers, reverse engineer the genetic cell machinery from these noisy datasets, interactive exploratory clustering methods, pipelines and gene clustering tools have to be specifically developed. Prior methods/tools for time series data, however, do not have the following four major ingredients in analytic and methodological view point: (i) principled time-series feature extraction methods, (ii) variety of manifold learning methods for capturing high-level view of the dataset, (iii) high-end automatic structure extraction, and (iv) friendliness to the biological user community. With a view to meet the requirements, we present AGCT (A Geometric Clustering Tool), a software package used to unravel the complex architecture of large-scale, non-necessarily synchronized time-series gene expression data. AGCT capture signals on exhaustive wavelet expansions of the data, which are then embedded on a low-dimensional non-linear map using manifold learning algorithms, where geometric proximity captures potential interactions. Post-processing techniques, including hard and soft information geometric clustering algorithms, facilitate the summarizing of the complete map as a smaller number of principal factors which can then be formally identified using embedded statistical inference techniques. Three-dimension interactive visualization and scenario recording over the processing helps to reproduce data analysis results without additional time. Analysis of the whole-cell Yeast Metabolic Cycle (YMC) moreover, Yeast Cell Cycle (YCC) datasets demonstrate AGCT's ability to accurately dissect all stages of metabolism and the cell cycle progression, independently of the time course and the number of patterns related to the signal. Analysis of Pentachlorophenol iduced dataset demonstrat how AGCT dissects data to identify two networks: Interferon signaling and NRF2-signaling networks.
Competing Interests: Commercial affiliation authors does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors have declared no competing interests exist.
Databáze: MEDLINE
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