Analysis techniques for microarray time-series data

摘要: We introduce new methods for the analysis of short-term time-series data, and apply them to gene expression data in yeast. These include (1) automated period detection a predominately cycling set (2) phase between phase-shifted cyclic sets. show how properly correct problem comparing correlation coefficents pairs sequences different lengths small alphabets. In particular, we that coefficient over alphabets size two can exhibit very counter-intuitive behavior when compared with Hamming distance. Finally, address predictability known regulators via analysis, less than 20% regulatory strong correlations Cho/Spellman By analyzing relationships, designed an edge function which identified candidate regulations greater fidelity standard methods.