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The first part, the exploratory data set (33%), was used to assist in generating the hypotheses.
In addition, evaluated genes were chosen by a data-driven approach, using the Affymetrix® data as an exploratory data set.
Differences were only claimed if hypotheses generated in the exploratory data set were confirmed in the confirmatory data set.
The short-term data was split into an exploratory data set and a confirmatory data set, and analysed using subject-specific and group-based methods.
fTo avoid using the same sample both in the exploratory analysis and the confirmatory model, the sample was ideally split into two data sets: (a) an exploratory data set of 69 units on which the exploratory factor analysis was performed and (b) a confirmatory data set of 40 units on which the confirmatory model was developed.
For the first part of the study, these patients were statistically evaluated as an exploratory data set (Cohort 1). 1 Among the 109 patients, two were omitted from statistical analyses as protocol violators.
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In the exploratory factor analysis in data set 2, one factor with an eigenvalue >1 (Kaiser criterion) was found.
In a multidimensional exploratory approach, a microarray data set is usually analyzed by multivariate analysis (MVA) among which Principal Components Analysis (PCA) is the most used.
Analysis proceeded in three stages: 1. Full data set: Following exploratory analysis, the final model used the fixed terms: site, species, and species × site interaction, as well as the random terms: provenance (species), provenance (species) × site, and replicate × species (site) (Table 4). 2.
These distributions were chosen after an exploratory analysis of the original data set.
This study is of a qualitative and exploratory nature, using both a small data set and two case studies.
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