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Figure 8 | Genome Biology

Figure 8

From: Boolean implication networks derived from large scale, whole genome microarray datasets

Figure 8

Boolean implication extraction process. The expression levels of each probeset are sorted and a step function fitted (using StepMiner) to the sorted expression level w minimizes the square error between the original and the fitted values. A threshold t is chosen, where the step crosses the original data. The region between t - 0.5 and t + 0.5 is classified as 'intermediate', the region below t - 0.5 is classified as 'low' and the region above t + 0.5 is classified as 'high'. The examples show probesets for two genes, CDH1 and CDC2. As can be seen, CDH1 has a sharp rise between 6 and 9 and the StepMiner algorithm was able to assign a threshold in this region. CDC2, however, is very linear, and the StepMiner algorithm assigns the threshold approximately in the middle of the line. A scatter plot is shown to illustrate the analysis. Each point in the scatter plot corresponds to a microarray experiment, where the value for the x-axis is CDC2 expression and the value for the y-axis is CDH1 expression. Boolean implication discovery analysis is performed on a pair of probesets, which ignores all the points that lie in the intermediate region and analyzes the four quadrants of the scatter plot. Four asymmetric relationships (low low, low high, high low, high high) are discovered, each corresponding to exactly one sparse quadrant in the scatter plot; and two symmetric relationships (equivalent and opposite) are discovered, each corresponding to two diagonally opposite sparse quadrants.

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