In order to answer new biological questions, high-throughput data generated by new biotechnologies can be very meaningful but require specific and adapted statistical treatments. Thus, in the context of abiotic stress signaling studies, understanding the integration of cascading mechanisms from stress perception to biochemical and physiological adjustments necessarily entails efficient and valid analysis of multilevel and heterogeneous data. In this chapter, we propose examples to manage, analyze, and integrate multi-omics heterogeneous data. This workflow suggests and follows different general biological questions or issues answered with detailed code, data analysis, multiple visualizations, and always followed by brief interpretations. We illustrated this using the mixOmics package for the R software, as it specifically provides tools to address vertical and horizontal data integration issues. In order to illustrate this workflow, we used the usual omics datasets biologists can generate (phenomics, metabolomics, proteomics, and transcriptomics). These data were collected from two organs (leaf rosettes, floral stems) of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. They are available in the R package WallOmicsData. The workflow presented here is not limited to Arabidopsis thaliana and can be applied to any plant species. It can even be largely deployed to whatever the organisms of interest and the biological questions may be.