Myocardial shape and deformation are two relevant descriptors for the study of cardiac function and can undergo strong interactions depending on diseases. Manifold learning provides low dimensional representations of these high-dimensional descriptors, but the choice of normalization can strongly affect the analysis. Besides, whether the shape normalization should include a scale factor is still an open question. In this paper, we investigate the influence of normalization choices on the study of the interactions between cardiac shape and deformation using Multiple Manifold Learning, a dimensionality reduction method that considers inter-and intra-descriptors link between samples. By studying the main variations of two different shape normalizations (one including scaling, the other one not) we observed that the scaled normalization concentrates variations of a given physiological characteristic on only one mode. The influence of the associated choice of the deformation normalization was evaluated by quantifying differences between the estimated low-dimensional spaces (one for each choice against a combination of both), revealing potential analysis biases that may arise depending on such choices.