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The broad and diverse information was validated by different experimental platforms.
TRAM is able to perform some useful data normalization methods to allow comparison of gene expression data obtained by different biological samples and/or by different experimental platforms.
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It could be explained by the different experimental platforms used (qRT-PCR versus different miRNA array systems), the batch effects that are inherent in the microarray experiment itself or the potential ethnic differences in the study populations.
We can integrate different experiments that measure the same biological entity, such as microarray studies measuring tumor versus normal gene expression differences on different experimental platforms.
Furthermore, wet-lab measurements in biology are complicated by the fact that many attributes are experimentally inaccessible, necessitating vastly different experimental platforms (e.g. in vivo, in vitro).
We exemplify these capabilities by achieving validation targets from two different experimental platforms.
However, since these studies often involve different mosquito strains, different experimental platforms and analysis by different statistical methods, comparison among treatments is challenging.
The data residing in Flynet includes data generated using different experimental platforms and under various experimental conditions, including different developmental stages, different cell lines and different antibodies.
The different experimental platforms permit the identification of different sets of genes associated with FT, which might impact the correlations of SNP-FT associations across platforms.
With microarray and similar data, small sample numbers and different experimental platforms can lead to highly variable results.
Further, our model has flexible data distribution assumption, enabling a unified interface for segmenting data generated from different experimental platforms.
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