Back to Search Start Over

Mining TCGA data using Boolean implications.

Mining TCGA data using Boolean implications.

Authors :
Sinha S
Tsang EK
Zeng H
Meister M
Dill DL
Source :
PloS one [PLoS One] 2014 Jul 23; Vol. 9 (7), pp. e102119. Date of Electronic Publication: 2014 Jul 23 (Print Publication: 2014).
Publication Year :
2014

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/.

Details

Language :
English
ISSN :
1932-6203
Volume :
9
Issue :
7
Database :
MEDLINE
Journal :
PloS one
Publication Type :
Academic Journal
Accession number :
25054200
Full Text :
https://doi.org/10.1371/journal.pone.0102119