Figure 1

(a) Overview of our method for determining regulatory coupling strengths between transcription factors and their putative target genes. Inputs are (i) a library of microarray expression data for a large number of conditions and (ii) genomewide (ChIP) occupancy data for one or more transcription factors. In the first step of our algorithm, a matrix of transcription factor activities is inferred by using regression analysis to explain the mRNA expression pattern under each condition in terms of the ChIP data for each transcription factor. In the second step, a matrix of regulatory coupling strengths is determined by computing the correlation between each transcription factor activity profile (TFAP) and the mRNA expression profile of each gene. (b) Examples of transcription factor activity profiles. The activity profiles of three transcription factors (Hap4, Ndd1, Ste12) are shown across stress response, pheromone response, and cell cycle [28–30]. Significant changes in activity of the TCA cycle regulator Hap4p occur mostly in metabolic stress conditions, while changes in the activity of the cell cycle regulator Ndd1p and the pheromone-dependent regulator Ste12p are associated with the cell cycle and signal transduction experiments, respectively. (c) Examples of scatter plots of ChIP binding log-ratio versus coupling factor. In the scatter plots, black dots denote unbound (B-) genes, red dots denote bound and coupled genes (B+/C+), while green dots denote genes that are bound but not coupled (B+/C-). A threshold of P = 10^-3 was used to determine significant binding as described in Lee et al. [4]. A threshold for coupling was determined by requiring a false discovery rate of 5%, as described in Methods.