Unlike PCA, OPLS-DA can identify sets of metabolites that best distinguish between predefined classes of samples.
To estimate significant features, i.e., peaks differentiating the studied classes of samples (green and fermented rooibos plant material), we propose the application of rPLR (robust pair-wise log-ratios) as proposed by Walach et al. [2].
The most important feature of the proposed approach is that, due to the probabilistic properties of the nonlinear transformation applied, the safe and failure classes of samples are clearly distinguishable and occupy a standard position in a plot.
Orthogonal partial least square discriminant analysis (OPLS-DA), a supervised multivariate regression technique, was performed to identify components that best differentiate between predefined classes of samples while dissecting orthogonal components which do not differentiate between these classes.
The OAO method is to design a classifier between any two classes of samples, so k k − 1)/2 classifiers need to be designed for samples of k classes.
Most of the CF studies compare two or more classes of samples, e.g., CF vs. non-CF [6], CF with low inflammation vs. high inflammation [16], stable CF vs. unstable CF [19], and CF vs. PCD vs. healthy subjects [20], such studies benefit from using supervised classification methods.
It is relatively less challenging to identify differentially expressed genes from two or more classes of samples.
All tests done were based on comparing the differences in normalized, mean log-transformed intensities between classes of samples.
The median survival time of 3 years was chosen as cut-off to balance both classes of samples.
Hence this bias is very useful in distinguishing the genes that are expressed differently in the two classes of samples.
