  In The Cancer Genome Atlas (TCGA) data set, there are many interesting
nonlinear dependencies between pairs of genes that reveal important
relationships and subtypes of cancer. Such genomic data analysis requires a
rapid, powerful and interpretable detection process, especially in a
high-dimensional environment. We study the nonlinear patterns among the
expression of pairs of genes from TCGA using a powerful tool called Binary
Expansion Testing. We find many nonlinear patterns, some of which are driven by
known cancer subtypes, some of which are novel.
