We then also checked for changes under removal of the most connected authors and found non-significant changes to the dataset properties.
Coefficients (also called loadings) can be used to determine the importance of individual descriptors to the variation in the dataset and so derive interpretations, but it is worth bearing in mind that PCA is not statistically robust [7] and changes to the dataset can substantially affect the PC composition, hindering their detailed chemical interpretation.
However, it is possible that the model may be overfitted, which means that a small change to the dataset could make the network structure change dramatically.
Unfortunately, making changes to the national clinical dataset is difficult and expensive, not only are changes required to SUS to make the data flow, but also disparate Trust systems have to be modified in order for the new data to be collected locally and exported into SUS.
Since the initial development of the OPERA models using only the curated PHYSPROP dataset, additional changes have been made to the datasets before rebuilding the models.
Systematically applying all the above changes to our representative dataset (Fairburn, Palmer, et al., in preparation) indicates that their impact on the clinical prevalence of eating disorder NOS would be modest.
Furthermore, the information in the thesaurus and in the exceptions is needed, in any case, to document the changes made in the datasets.
In contrast, the DCC, Dataverse, and the UK Natural Environment Research Council (NERC) insist that any change to a dataset should trigger a new identifier.
To assess metabolic changes in the dataset, a common processing strategy was adopted throughout the analysis.
To interpret functional changes in the dataset, we applied two pathway analysis programs, Metacore and GSEA, with different complementary pathway-classification properties.
