Nuclear reactor's transients computational modeling under an uncertainty framework creates many challenges related to the potential large number of inputs and outputs to be considered, their interactions and dependencies. In the particular case of Rod Ejection Accident (REA) in Pressurized Water Reactors (PWR) strong multi-physics coupling effects occur between neu-tronics, fuel-thermal and thermal-hydraulics. APOLLO3 R neutronic code using two group diffusion modeling and FLICA4 thermal-hydraulic code using axial multi-channel 1D model-ing are coupled in the framework of CORPUS Best Estimate multi-physics tool to model the REA. CORPUS, APOLLO3 R and FLICA4 are developed at CEA and are used for the first time in a REA uncertainty analysis. Different statistical tools are explored and combined in an uncertainty analysis methodology using R language. The methodology is developed and tested on a small scale geometry representative of a PWR core. A total of 22 inputs are considered spanning neutronics, fuel-thermal and thermal-hydraulics. Three scalar and one functional outputs are studied. The methodology consists of different steps. First, a screening process based on dependence measures is performed in order to identify an important reduced input subspace. Second, a design of experiments is created by preserving good space-filling properties in both the original and reduced input spaces. This design is used to train kriging surrogate models only on the reduced subspaces. The kriging models are used then for brute force Monte Carlo (MC) uncertainty propagation and global sensitivity analysis by estimating Shapley indices. Concerning the functional output Principal Components Analysis (PCA) was used to reduce its dimension. The results show that the methodology manages to identify two subsets of important inputs and estimates the histograms and Shapley indices for both scalar and functional outputs. This will motivate the application of the derived methodology to a full core design for transient analysis purpose.