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The dataset of this microarray study was deposited in Gene Expression Omnibus (GEO) with the series accession number GSE16397.
In order to get an insight into the global structure of the complex dataset of this microarray investigation, we performed a principal component analysis (PCA) on a reduced dataset (see Methods) using the steady-state mRNA levels determined by the application of a common reference hybridization strategy (see Methods).
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This resulted in a dataset of 72 microarray measurements of 25,280 genes.
Furthermore, we analyzed the separate datasets of the microarrays with special emphasis on the expression profiles of immune-related genes.
To demonstrate and assess the applicability of the model to biological data we first of all applied it on a dataset of yeast microarray measurements by [4].
We generated a transcriptional dataset of 87 microarray samples spanning eight APC populations across human vagina, skin and blood.
The authors of the NetsTensor algorithm reported that the algorithm took 200 hours (more than eight days) to finish on a dataset of 131 microarray datasets [ 5].
By re-analyzing a dataset of microarray data derived from islets of type 2 diabetes patients (Dominguez et al., 2011), we found that rab1a mRNA expression was significantly decreased (P < 0.001, Fig. S1A).
We tested the approach with a published dataset of microarray experiments in yeast.
Greco et al. [ 7] investigated tissue-selective expression patterns with an integrated dataset of microarray profiles publicly available at the GEO database.
The dataset of microarray results have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO series accession number: GSE57709 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi acc=GSE57709).nih.gov/geo/query/acc.cgi acc=GSE57709
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