Motivation: Traditionally, microarrays were almost exclusively employed for the genome-wide analysis of differential gene expression. But nowadays, their scope of application has been extended to various genomic features, such as microRNAs, proteins, and DNA methylation. Most available methods for the visualization of these datasets are focused on individual platforms and are not capable of integratively visualizing multiple microarray datasets from cross-platform studies. Above all, there is a demand for methods that can visualize genomic features that are not directly linked to protein-coding genes, such as regulatory RNAs (e.g., microRNAs) and epigenetic alterations (e.g., DNA methylation), in a pathway-centered manner. Results: We present a novel pathway-based visualization method that is especially suitable for the visualization of high-throughput datasets from multiple different microarray platforms which were employed for the analysis of diverse genomic features in the same set of biological samples. The proposed methodology includes concepts for linking DNA methylation and microRNA expression datasets to canonical signaling and metabolic pathways. We further point out strategies for displaying data from multiple proteins and protein modifications corresponding to the same gene. Ultimately, we show how data from four distinct platform types (mRNA, miRNA, protein, and DNA methylation arrays) can be integratively visualized in the context of canonical pathways. Availability: The described method is implemented as part of the InCroMAP application that is freely available at http://www.cogsys.cs.uni-tuebingen.de/software/InCroMAP/. Contact: mailto:clemens.wrzodek@uni-tuebingen.de
@article{Wrzodek2012_2,
author = {Wrzodek, Clemens and Eichner, Johannes and Zell, Andreas},
title = {Pathway-based visualization of cross-platform microarray datasets.},
journal = {Bioinformatics},
year = {2012},
volume = {28},
pages = {3021--3026},
number = {23},
month = sep,
abstract = {Motivation: Traditionally, microarrays were almost exclusively employed
for the genome-wide analysis of differential gene expression. But
nowadays, their scope of application has been extended to various
genomic features, such as microRNAs, proteins, and DNA methylation.
Most available methods for the visualization of these datasets are
focused on individual platforms and are not capable of integratively
visualizing multiple microarray datasets from cross-platform studies.
Above all, there is a demand for methods that can visualize genomic
features that are not directly linked to protein-coding genes, such
as regulatory RNAs (e.g., microRNAs) and epigenetic alterations (e.g.,
DNA methylation), in a pathway-centered manner. Results: We present
a novel pathway-based visualization method that is especially suitable
for the visualization of high-throughput datasets from multiple different
microarray platforms which were employed for the analysis of diverse
genomic features in the same set of biological samples. The proposed
methodology includes concepts for linking DNA methylation and microRNA
expression datasets to canonical signaling and metabolic pathways.
We further point out strategies for displaying data from multiple
proteins and protein modifications corresponding to the same gene.
Ultimately, we show how data from four distinct platform types (mRNA,
miRNA, protein, and DNA methylation arrays) can be integratively
visualized in the context of canonical pathways. Availability: The
described method is implemented as part of the InCroMAP application
that is freely available at \url{http://www.cogsys.cs.uni-tuebingen.de/software/InCroMAP/}.
Contact: \url{mailto:clemens.wrzodek@uni-tuebingen.de}},
doi = {10.1093/bioinformatics/bts583},
eprint = {http://bioinformatics.oxfordjournals.org/content/28/23/3021.full.pdf+html},
keywords = {Integration of omics data, Integration, omics, data, Visualization,
Microarray data, Pathway, Pathways, Data integration, Microarray
data analysis, MicroRNA},
pdf = {http://bioinformatics.oxfordjournals.org/content/28/23/3021.full.pdf},
url = {http://bioinformatics.oxfordjournals.org/content/28/23/3021}
}