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

Autor: Hiroaki Kitano, Ken-ichi Aisaki, Richard Nock, Cedric Heimhofer, Taketo Akama, Satoshi Kitajima, Keigo Oka, Carlin R. Connell, Samik Ghosh, Frank Nielsen, Jun Kanno, Natalia Polouliakh, Kazuhiro Shibanai
Jazyk: angličtina
Rok vydání: 2020
Předmět:
0301 basic medicine
Computer science
Gene Expression
Datasets as Topic
computer.software_genre
Topology
Biochemistry
Mice
Random Allocation
0302 clinical medicine
Cell Cycle and Cell Division
Manifolds
Multidisciplinary
Gene Ontologies
Applied Mathematics
Simulation and Modeling
Systems Biology
Cell Cycle
Eukaryota
Genomics
Markov Chains
Liver
Cell Processes
Physical Sciences
Medicine
Data mining
Algorithms
Research Article
Cell Physiology
Pentachlorophenol
Science
Systems biology
Feature extraction
Wavelet Analysis
Saccharomyces cerevisiae
Research and Analysis Methods
03 medical and health sciences
Clustering Algorithms
Genetics
Animals
Cluster analysis
SIGNAL (programming language)
Nonlinear dimensionality reduction
Organisms
Fungi
Biology and Life Sciences
Computational Biology
Proteins
Cell Biology
Genome Analysis
Yeast
Cell Metabolism
030104 developmental biology
Gene Expression Regulation
Interferons
computer
030217 neurology & neurosurgery
Mathematics
Software
Zdroj: PLoS ONE
PLoS ONE, 15 (7)
PLoS ONE, Vol 15, Iss 7, p e0233755 (2020)
ISSN: 1932-6203
Popis: 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.
PLoS ONE, 15 (7)
ISSN:1932-6203
Databáze: OpenAIRE
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