Cross-platform prediction of gene expression signatures

Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing n...

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Bibliographic Details
Published in:PloS one 2013-11, Vol.8 (11), p.e79228-e79228
Main Authors: Lin, Shu-Hong, Beane, Lauren, Chasse, Dawn, Zhu, Kevin W, Mathey-Prevot, Bernard, Chang, Jeffrey T
Format: Article
Language:English
Subjects:
Activation
Bioinformatics
Biology
Breast cancer
Cell Line, Tumor
DNA microarrays
DNA probes
Gene expression
Gene Expression Profiling - instrumentation
Gene Expression Profiling - methods
Gene Expression Regulation
Gene mapping
Genes
Genome-Wide Association Study
Genomes
Genomics
Humans
Hybridization
Mathematical models
Melanoma
Oligonucleotide Array Sequence Analysis - instrumentation
Oligonucleotide Array Sequence Analysis - methods
Pharmacology
Platforms
Probes
Ribonucleic acid
RNA
Science
Signatures
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Summary:Gene expression signatures can predict the activation of oncogenic pathways and other phenotypes of interest via quantitative models that combine the expression levels of multiple genes. However, as the number of platforms to measure genome-wide gene expression proliferates, there is an increasing need to develop models that can be ported across diverse platforms. Because of the range of technologies that measure gene expression, the resulting signal values can vary greatly. To understand how this variation can affect the prediction of gene expression signatures, we have investigated the ability of gene expression signatures to predict pathway activation across Affymetrix and Illumina microarrays. We hybridized the same RNA samples to both platforms and compared the resultant gene expression readings, as well as the signature predictions. Using a new approach to map probes across platforms, we found that the genes in the signatures from the two platforms were highly similar, and that the predictions they generated were also strongly correlated. This demonstrates that our method can map probes from Affymetrix and Illumina microarrays, and that this mapping can be used to predict gene expression signatures across platforms.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0079228