Raw data and supervised statistical multivariate analyses of the physiological and biochemical response of three wheat genotypes to mild water deficit under elevated CO2 and high temperatures [Dataset]

Comprises 6 files: - ‘Figure S1_Bendou et al.docx’ comprises one single page: ‘Figure S1. Structure of the raw LEYQ data set and multivariate analyses applied to the data set using the ade4 package.’ - ‘TS1_TS5_Supplementary material_Bendou et al.xlsx’ comprises 5 sheets: ‘Table S1. Raw data set.’ ‘Table S2. Extreme outliers of the raw data set.’ ‘Table S3. Shapiro tests.’ ‘Table S4. Levene tests.’ ‘Table S5. Selection of tests (two-way, one-way Anova or T test) to be performed (if needed) according to the presence or absence of extreme outliers and the compliance with the assumptions of normal distribution and homocedasticity.’ -‘TS6_TS7_Supplementary material_Bendou et al.xlsx’ comprises 2 sheets: ‘Table S6. List of variables showing statistical significance after performing, firstly, two-way ANOVA and then one-way ANOVA or T test if there was no statistical significance for an interaction effect between factors.’ ‘Table S7. Summary of post-hoc tests.’ -‘TS8_TS10_Supplementary material_Bendou et al.xlsx’ comprises 3 sheets ‘Table S8. Dudi output elements of the BCA.’ ‘Table S9. Results of the BCA Monte-Carlo test (randtest in ade4).’ ‘Table S10. BCA column normed scores of the variables of the four meaningful blocks. -‘TS11_TS12_Supplementary material_Bendou et al.xlsx’ comprises 2 sheets ‘Table S11. Result of the COIA Monte-Carlo test (randtest in ade4).’ ‘Table S12. COIA loadings for the variables of the blocks of Yield and Quality.’ -‘TS13_TS15_Supplementary material_Bendou et al.xlsx’ comprises 3 sheets ‘Table S13. MBPLS regression using the grain yield as the dependent data block and flag leaves and ears as the explanatory blocks.’ ‘Table S14. Projection of the dependent variables on the LCs and loadings of the explanatory variables in the MBPLS regression.’ ‘Table S15. Determination of the cumulated variable importance index (vipc) to obtain the variables of the explanatory blocks (i.e., leaves and ears) that best explained the dependent block (i.e., grain yield) in the MBPLS regression.