A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions.  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.  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.
AGCT
PloS one
D000465:Algorithms; D000818:Animals; D002453:Cell Cycle; D019295:Computational Biology; D066264:Datasets as Topic; D015870:Gene Expression; D005786:Gene Expression Regulation; D008099:Liver; D008390:Markov Chains; D051379:Mice; D010416:Pentachlorophenol; D011897:Random Allocation; D012441:Saccharomyces cerevisiae; D012984:Software; D049490:Systems Biology; D058067:Wavelet Analysis
Nock Richard R ;Polouliakh Natalia N 0000-0001-8266-2697;Nielsen Frank F ;Oka Keigo K ;Connell Carlin R CR ;Heimhofer Cedric C ;Shibanai Kazuhiro K ;Ghosh Samik S ;Aisaki Ken-Ichi KI ;Kitajima Satoshi S ;Kanno Jun J ;Akama Taketo T ;Kitano Hiroaki H 