Table 1 Transformation of data for regression analysis
| Â | RESPONSE VARIABLE | PREDICTOR VARIABLE |
|---|---|---|
A. Absolute deviation from a reference state | y
i
= | x i = X i - X ir |
B. Relative deviation from a reference state |
|
|
C. Lotka-Volterra system |
| x i = X i |
- We assume the general linear model is y = ai 0+ Σ(a
ij
x
j
). The X
i
denote experimental time series data for metabolite i, while the slopes (
) are estimated from the smooth output functions of the artificial neural network that had been trained on the experimental data. Subscript r denotes the value of the metabolite at a reference state. Linearization options I and II are included in transformations A and B respectively, assuming that the reference state is a steady state. For a piecewise linear linearization (option III), the data may be transformed following either A or B.