Detection of Perturbation Phases and Developmental Stages in Organisms from DNA Microarray Time Series Data

• Published in: PloS one Vol. 6; no. 12; p. e27948
• Main Authors: Rooman, Marianne, Albert, Jaroslav, Dehouck, Yves, Haye, Alexandre
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 15.12.2011; Public Library of Science (PLoS)
• Subjects: Animals; Biology; Breast cancer; Cell cycle; Ciona intestinalis; Cluster Analysis; Clustering; Comparative analysis; Deoxyribonucleic acid; Developmental stages; Diauxie; Distance measurement; DNA; DNA microarrays; Drosophila melanogaster; E coli; Escherichia coli; Eukaryotes; Extracellular matrix; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Developmental; Genes; Genomics; Insects; Invariants; Molecular biology; Oligonucleotide Array Sequence Analysis - methods; Organisms; Oxidative stress; Phases; Regression analysis; Rodents; Saccharomyces cerevisiae; Statistics as Topic; Stress response; Time Factors; Time series; Transition points; Vinegar
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0027948

Available DNA microarray time series that record gene expression along the developmental stages of multicellular eukaryotes, or in unicellular organisms subject to external perturbations such as stress and diauxie, are analyzed. By pairwise comparison of the gene expression profiles on the basis of a translation-invariant and scale-invariant distance measure corresponding to least-rectangle regression, it is shown that peaks in the average distance values are noticeable and are localized around specific time points. These points systematically coincide with the transition points between developmental phases or just follow the external perturbations. This approach can thus be used to identify automatically, from microarray time series alone, the presence of external perturbations or the succession of developmental stages in arbitrary cell systems. Moreover, our results show that there is a striking similarity between the gene expression responses to these a priori very different phenomena. In contrast, the cell cycle does not involve a perturbation-like phase, but rather continuous gene expression remodeling. Similar analyses were conducted using three other standard distance measures, showing that the one we introduced was superior. Based on these findings, we set up an adapted clustering method that uses this distance measure and classifies the genes on the basis of their expression profiles within each developmental stage or between perturbation phases.