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Mining TCGA data using Boolean implications.
Mining TCGA data using Boolean implications.
- 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/.
- Subjects :
- DNA Copy Number Variations
DNA Methylation
Female
Gene Expression Regulation, Neoplastic
Humans
Internet
Mutation
Reproducibility of Results
Brain Neoplasms genetics
Computational Biology methods
Cystadenoma, Serous genetics
Data Mining methods
Glioblastoma genetics
Ovarian Neoplasms genetics
Subjects
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