Pearson's correlations for the aggregate dataset are shown in Table 1.
Finally, the normalization by gene coverage and mRNA-seq discussed in the previous section was performed on the aggregate dataset.
Multiple analytic methods including constructing contextual variables, ordinary least squares (OLS) regression analysis for the aggregate dataset and for individual communities, and multilevel modeling, were used to explore community contextual effects on beetle-related activeness.
Neighborhood Correlation obtains much better performance on these aggregate datasets: the best performance of the rewired network (F max =0.85, avg. linkage) is substantially greater than that of sequence similarity (F max =0.42, avg. linkage).
Because these aggregate datasets were both de-identified and decoupled from each other as a means for maximizing participant confidentiality (Corti and Thompson 2012; Kum and Ahalt 2013), it was not possible to conduct complex, multivariable analyses.
Within this aggregate dataset of 267 GBMs, the three previously defined molecular glioma subgroups were robustly detected as defined by over-expression of a series of related genes: Neurogenesis/HC1A (a.k.a. ProNeural), Mitotic/HC2A (a.k.a. Proliferative), and Extra-Cellular Matrix related/HC2B (a.k.a. Mesenchymal).
Advances in the area of big data have attempted to address the non-uniformity in aggregate datasets by using different data models.
In addition, we gathered data at national level between 1950 and 1989, allowing us to aggregate datasets to the current (2013) territorial boundaries and thus accounting for political border changes that occurred during our study period.
Data that passed the verification process were then placed into sport-specific aggregate datasets for use by external researchers.
Aggregate datasets were used to generate Figure 1D, 5 and Additional File 1.
