We purposefully adopted an analysis strategy that stripped away differences between UCB-derived G0 and G1 cells thus eliminating any genomic difference that may be attributed to the cell cycle status of these functionally similar groups of cells.
We successfully adopted an analysis workflow able to overcome a major criticality in high-throughput studies, that is to find robust, reproducible and biologically sound results [ 16, 46].
Given uncertainty in expected effect sizes, we adopted an interim analysis approach in which a flexible stopping rule was applied with appropriate Type I error correction (Strube, 2006).
At the first level of analysis, we adopted a parametric analysis according to Büchel et al. [ 23].
As a purely epistatic model is a challenge for the analysis in itself, we adopted a further analysis method, i.e. the multifactor dimensionality reduction (MDR [44], [52].
Hence, we adopted a quantitative analysis in our case study [57].
To test the sequential mediation model (Hypotheses 5) and the moderated mediation model (Hypotheses 7), we adopted a bootstrapping analysis by using the PROCESS program in SPSS provided by Hayes (2013), which could test the conditional indirect effect to provide support for the sequential mediation and moderated mediation model.
We adopted a sensitivity analysis approach in which we contrasted results obtained under two very different scenarios, representing two extreme possible endpoints of average GWAS panel SNP composition.
To compare our results with those reported by Riggs et al. (2006), we adopted a similar analysis method.
In this work, we adopted a supervised analysis schema: l1 l2 FS regularization with double optimization framework, set in a nested cross-validation structure (l1 l2 FS ).
