Skip to main content


Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Table 2 Statistical testing of antagonistic interactions

From: Interactions of a pesticide/heavy metal mixture in marine bivalves: a transcriptomic assessment

n 49 45 158 72
Fit of CA     
R^2 0.73 0.64 0.74 0.77
P-value 5.59E-012 2.96E-009 2.48E-044 1.01E-020
CA vs A     
R^2 0.79 0.88 0.89 0.85
Chi-test 1.27E-003 2.30E-025 2.64E-030 4.05E-008
A vs DL     
R^2 0.78 0.88 0.89 0.85
Chi-test 0.30 0.89 0.94 0.23
Fit of IA     
R^2 0.75 0.78 0.74 0.64
P-value 8.52E-013 5.25E-015 3.06E-043 4.05E-014
IA vs A     
R^2 0.78 0.85 0.79 0.85
Chi-test 1.10E-002 9.63E-011 1.17E-009 4.81E-015
A vs DL     
R^2 0.78 0.85 0.74 0.84
Chi-test 0.63 1 1 1
  1. The Mixtox software [7] was utilized to evaluate antagonistic interactions in binary mixtures fitting experimental data to both the CA and IA reference models and their deviation models describing antagonistic interactions, either through the whole dose range (A) or varying across the dose levels (DL). R^2 represents the amount of the total data variance explained by each tested model. P-value represents the significance of data regression against the "null hypothesis" of no relationship between increasing doses and effects for the whole dataset. Chi-test represents the p-value of the Chi-square distribution test to assess if a significantly better fit is achieved by going to the next level of the nested models (i.e. CA vs. A and A vs. DL). The A models always provided a better description of the data than their respective parent CA or IA models, while there were no significant improvements by using models explaining a dose level dependent antagonism (DL).