title: Statistical stage transition detection method for small sample gene expression time series data.
abstract: In terms of their internal (genetic) and external (phenotypic) states, living cells are always changing at varying rates. Periods of stable or low rate of change are often called States, Stages, or Phases, whereas high-rate periods are called Transitions or Transients. While states and transitions are observed phenotypically, such as cell differentiation, cancer progression, for example, are related with gene expression levels. On the other hand, stages of gene expression are definable based on changes of expression levels. Analyzing relations between state changes of phenotypes and stage transitions of gene expression levels is a general approach to elucidate mechanisms of life phenomena. Herein, we propose an algorithm to detect stage transitions in a time series of expression levels of a gene by defining statistically optimal division points. The algorithm shows detecting ability for simulated datasets. An annotation based analysis on detecting results for a dataset of initial development of Caenorhabditis elegans agrees with that are presented in the literature.
authors: Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto, Tokyo 135-0064, Japan. Electronic address: tominaga@cbrc.jp.
authors: Daisuke
authors: D
authors: Tominaga
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000235:genetics / Q000254:growth & development; D003627:Data Interpretation, Statistical; D018507:Gene Expression Regulation, Developmental / Q000235:genetics*; D010641:Phenotype
keywords: Caenorhabditis elegans; Development; Gene ontology; Information criterion