Merging microarray studies to identify a common gene expression signature to several structural heart diseases

BioData Mining
Olga FajardaJosé Luís Oliveira

Abstract

Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used. Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed gen...Continue Reading

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Datasets Mentioned

BETA
GSE1145
GSE21610
GSE115574
GPL6244
GPL570
GPL11532

Software Mentioned

sva package
R package caret
oligo package
STRING
R
PANTHER
Bioconductor
limma

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