Plant Boolean Implication Network

Numerous gene expression datasets from diverse plant tissue samples have been already deposited in the public domain. There have been several attempts to do large scale meta-analyses of all of these datasets. Most of these analyses summarize pairwise gene expression relationships using correlation, or identify differentially expressed genes in two conditions. We propose here a new large scale meta-analysis of all of the publicly available plant datasets to identify Boolean logical relationships between genes. Boolean logic is a branch of mathematics that deals with two possible values. In the context of gene expression datasets we use qualitative high and low expression values. A strong logical relationship between genes emerges if at least one of the quadrants is sparsely populated. 4,306 published Arabidopsis microarray samples assayed on the GPL198 were re-analyzed. RMA was used to normalize the RAW CEL files all together.