A supervised partial least squares discriminant analysis (PLS-DA) approach will be used to compare the variance of metabolite concentrations between the two sample classes (diet: control and pea fibre).
OPLS-DA models were calculated in turn for (i) separation between the three sample classes (control, S. Typhi infected, and S. Paratyphi A infected), and (ii) for pairwise comparisons (control vs S. Typhi, control vs S. Paratyphi A, and S. Typhi vs S. Paratyphi A).
It is known that the pairwise constraint provides the relationship between two samples, which tells whether these samples belong to the same class or not.
While considering different networks, the Core genes were determined by the degree differences between two class samples [ 16].
To reduce the effect of such genes on the data normalization step, Robinson and Oshlack reported a simple but effective global scaling method, the trimmed mean of M values (TMM) method, where a scaling factor for the normalization is calculated as a weighted trimmed mean of the log ratios between two classes of samples (i.e., Samples A vs. B) [ 16].
Variables (metabolites) are extracted that discriminates between the sample classes (the method is hence called OPLS-DA).
The difference is that, while GSEA gives only an overall enrichment score of a pathway between two classes of samples, ASSESS provides an enrichment score of the pathway for each sample.
Three metabolites, in three samples from each sample group that were statistically significant in differentiating between sample classes using pattern recognition modelling, were selected for confirmation using unprocessed chromatographic data.
Paired miRNA and mRNA expression profiling provides an excellent platform for capturing those miRNA expression changes between two classes of samples that lead, positively or negatively, to the changes in mRNA expressions between the two classes of samples.
