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Philos Trans A Math Phys Eng Sci. 2014 Apr 21;372(2016):20130136. doi: 10.1098/rsta.2013.0136. Print 2014 May 28.

On integrating multi-experiment microarray data.

Author information

1
Bioinformatics and Medical Informatics Group, Biomedical Research Foundation, Academy of Athens, , 4 Soranou Ephessiou 115 27, Greece.

Abstract

With the extensive use of microarray technology as a potential prognostic and diagnostic tool, the comparison and reproducibility of results obtained from the use of different platforms is of interest. The integration of those datasets can yield more informative results corresponding to numerous datasets and microarray platforms. We developed a novel integration technique for microarray gene-expression data derived by different studies for the purpose of a two-way Bayesian partition modelling which estimates co-expression profiles under subsets of genes and between biological samples or experimental conditions. The suggested methodology transforms disparate gene-expression data on a common probability scale to obtain inter-study-validated gene signatures. We evaluated the performance of our model using artificial data. Finally, we applied our model to six publicly available cancer gene-expression datasets and compared our results with well-known integrative microarray data methods. Our study shows that the suggested framework can relieve the limited sample size problem while reporting high accuracies by integrating multi-experiment data.

KEYWORDS:

composite likelihood; gene expression; integrative genomics; partition modelling

PMID:
24751870
PMCID:
PMC3996576
DOI:
10.1098/rsta.2013.0136
[Indexed for MEDLINE]
Free PMC Article

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