To evaluate the proposed method, we applied the GP predictor to a meta dataset summarizing major G-quadruplex experiment data available as of today.
We therefore compiled and analyzed a meta-dataset of 362 published organic conventional comparative crop yields.
We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data.
With the increasing interest in examining outlier gene expression profiles in very large-scale meta-datasets, the GTI should complement the discovery of oncogenic genes and facilitate the identification of novel therapeutic targets.
However, the results must be considered in the context of the meta-dataset.
By analyzing a large tissue and cell line meta-dataset, we identified GPCRs, including Gi-coupled receptors, with evidence of expression in human pluripotent stem cells.
We used a modified subset of the full meta-dataset discussed by Marvier et al. [13] that is available at http://delphi.nceas.ucsb.edu/btcrops.ucsb.edu/btcrops
In meta-dataset like Connectivity Map's reference collection, each experiment only provided the phenotype this research group was interested in; other responses were ignored in most time.
For the microarray meta-dataset analysis, all Affymetrix CEL files were downloaded from NCBI Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) for the U133 plus 2.0 array platform from published datasets.
We constructed a meta-dataset of hES and hiPS cells, fibroblasts and 100 somatic normal tissues from publicly available data from the same DNA-microarray platform (U133 plus 2.0 array) [3], [29].
As a result, we created a meta-dataset, all based on the Affymetrix HG-U95Av2 platform, which to our knowledge was the platform covering the broadest spectrum of tumor samples.
