Scaling in Counter Expressed Gene Networks Constructed from Gene Expression Data

We study counter expressed gene networks constructed from gene-expression data obtained from many types of cancers. The networks are synthesized by connecting vertices belonging to each others' list of K-farthest-neighbors, with K being an a priori selected non-negative integer. In the range of K corresponding to minimum homogeneity, the degree distribution of the networks shows scaling. Clustering in these networks is smaller than that in equivalent random graphs and remains zero till significantly large K. Their small diameter, however, implies small-world behavior which is corroborated by their eigenspectrum. We discuss implications of these findings in several contexts.