Mining TCGA Data Using Boolean Implications
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Sinha-2014-Mining TCGA Data Usi.pdf
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Author(s)
Sinha, Subarna
Tsang, Emily K.
Zeng, Haoyang
Meister, Michela
Dill, David L.
Date Issued
July 2014
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Sinha, Subarna, Emily K. Tsang, Haoyang Zeng, Michela Meister, and David L. Dill. “Mining TCGA Data Using Boolean Implications.” Edited by Vladimir B. Bajic. PLoS ONE 9, no. 7 (July 23, 2014): e102119.
Version
Final published version
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/.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0102119
