Statistical Data Mining for Symbol Associations in Genomic Databases
International Journal of Genetics and Genomics
Volume 2, Issue 6, December 2014, Pages: 97-104
Received: Nov. 10, 2014; Accepted: Nov. 28, 2014; Published: Dec. 2, 2014
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Bernard Ycart, Université Grenoble-Alpes, Grenoble, France; Laboratoire Jean Kuntzmann, CNRS UMR5224, Grenoble, France; Laboratoire d'Excellence TOUCAN, Toulouse, France
Frederic Pont, Laboratoire d'Excellence TOUCAN, Toulouse, France; INSERM UMR1037-Cancer Research Center of Toulouse, Toulouse, France; Université Toulouse III Paul Sabatier, Toulouse, France; ERL 5294 CNRS, Toulouse, France
Jean-Jacques Fournie, Laboratoire d'Excellence TOUCAN, Toulouse, France; INSERM UMR1037-Cancer Research Center of Toulouse, Toulouse, France; Université Toulouse III Paul Sabatier, Toulouse, France; ERL 5294 CNRS, Toulouse, France
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A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test assesses the significance of a group of symbols when found in several genesets of a given database. To each pair of symbols, a p-value depending on the frequency of the two symbols and on the number of joint occurrences, is associated. All pairs with p-values below a certain threshold define a graph structure on the set of symbols. The cliques of that graph are significant symbol associations, linked to a set of genesets where they can be found. The method can be applied to any database, and is illustrated on the MSigDB C2 database. Many of the symbol associations detected in C2 or in non-specific selections correspond to already known interactions. On more specific selections of C2, many previously unknown symbol associations have been detected. These associations unveal new candidates for gene or protein interactions, needing further investigation for biological evidence.
Genomic Databases, Protein-Protein Interaction, Frequent Itemset Searching, P-Value Graph
To cite this article
Bernard Ycart, Frederic Pont, Jean-Jacques Fournie, Statistical Data Mining for Symbol Associations in Genomic Databases, International Journal of Genetics and Genomics. Vol. 2, No. 6, 2014, pp. 97-104. doi: 10.11648/j.ijgg.20140206.11
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