title: Mining TCGA data using Boolean implications.
abstract: Boolean implications (if-then rules) provide a conceptually simple, uniform and highly scalable way to find associations between pairs of random variables. In this paper, we propose to use Boolean implications to find relationships between variables of different data types (mutation, copy number alteration, DNA methylation and gene expression) from the glioblastoma (GBM) and ovarian serous cystadenoma (OV) data sets from The Cancer Genome Atlas (TCGA). We find hundreds of thousands of Boolean implications from these data sets. A direct comparison of the relationships found by Boolean implications and those found by commonly used methods for mining associations show that existing methods would miss relationships found by Boolean implications. Furthermore, many relationships exposed by Boolean implications reflect important aspects of cancer biology. Examples of our findings include cis relationships between copy number alteration, DNA methylation and expression of genes, a new hierarchy of mutations and recurrent copy number alterations, loss-of-heterozygosity of well-known tumor suppressors, and the hypermethylation phenotype associated with IDH1 mutations in GBM. The Boolean implication results used in the paper can be accessed at http://crookneck.stanford.edu/microarray/TCGANetworks/.
authors: Department of Computer Science, Stanford University, Stanford, California, United States of America.
authors: Subarna
authors: S
authors: Sinha
authors: Biomedical Informatics Program, Stanford University, Stanford, California, United States of America.
authors: Emily K
authors: EK
authors: Tsang
authors: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors: Haoyang
authors: H
authors: Zeng
authors: Department of Computer Science, Stanford University, Stanford, California, United States of America.
authors: Michela
authors: M
authors: Meister
authors: Department of Computer Science, Stanford University, Stanford, California, United States of America.
authors: David L
authors: DL
authors: Dill
mesh_terms: D001932:Brain Neoplasms / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D018293:Cystadenoma, Serous / Q000235:genetics*; D056915:DNA Copy Number Variations; D019175:DNA Methylation; D057225:Data Mining / Q000379:methods*; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D005909:Glioblastoma / Q000235:genetics*; D006801:Humans; D020407:Internet; D009154:Mutation; D010051:Ovarian Neoplasms / Q000235:genetics*; D015203:Reproducibility of Results