Analysis techniques for microarray time-series data

We address possible limitations of publicly available data sets of yeast gene expression. We study the predictability of known regulators via time-series analysis, and show that less than 20% of known regulatory pairs exhibit strong correlations in the Cho/Spellman data sets. By analyzing known regulatory relationships, we designed an edge detection function which identified candidate regulations with greater fidelity than standard correlation methods. We develop general methods for integrated analysis of coarse time-series data sets. These include 1) methods for automated period detection in a predominately cycling data set and 2) phase detection between phase-shifted cyclic data sets. We show how to properly correct for the problem of comparing correlation coefficients between pairs of sequences of different lengths and small alphabets. Finally, we note that the correlation coefficient of sequences over alphabets of size two can exhibit very counterintuitive behavior when compared with the Hamming distance.