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DEMs between sample classes were identified, considering the whole set of samples and the smaller group of per-patient matched samples in parallel.
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Differential gene expression analysis between sample classes was performed on the matrix containing the 5142 filtered genes using Gene Expression Profile Analysis Suite (GEPAS).
A resampling-based meta-analysis strategy, which involves the use of resampling and application of distribution statistics in combination to assess the degree of significance in differential expression between sample classes, was developed.
The hyperplane is said to be optimal when the separating margin between the sample classes is maximal.
Classes within schools were sampled systematically, and all students attending the sampled classes were examined.
Classes within schools were sampled systematically and all students attending the sampled classes were examined.
In the second principal component (PC2), however, a higher degree of separation between the sampled classes is obvious.
Therefore, differential expression between sample classes may not be always indicative of the relative contribution of a gene to sample separation.
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.
1. Metabolite Fingerprinting (also known as the chemometric approach) uses the spectral pattern as a whole to determine the spectral features that are statistically different between sample classes (e.g. cases versus controls; exposed (e.g. to dietary, environmental risk factors etc).
The two statistical modelling approaches are designed to search for multi-gene markers that maximise the distinction between sample classes.
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