Multiblock principal component analysis: an efficient tool for analyzing metabolomics data which contain two influential factors
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Principal component analysis (PCA) is probably one of the most used methods for exploratory data analysis. However, it may not be always effective when there are multiple influential factors. In this paper, the use of multiblock PCA for analysing such types of data is demonstrated through a real metabolomics study combined with a series of data simulating two underlying influential factors with different types of interactions based on 2 × 2 experiment designs. The performance of multiblock PCA is compared with those of PCA and also ANOVA-PCA which is another PCA extension developed to solve similar problems. The results demonstrate that multiblock PCA is highly efficient at analysing such types of data which contain multiple influential factors. These models give the most comprehensive view of data compared to the other two methods. The combination of super scores and block scores shows not only the general trends of changing caused by each of the influential factors but also the subtle changes within each combination of the factors and their levels. It is also highly resistant to the addition of ‘irrelevant’ competing information and the first PC remains the most discriminant one which neither of the other two methods was able to do. The reason of such property was demonstrated by employing a 2 × 3 experiment designs. Finally, the validity of the results shown by the multiblock PCA was tested using permutation tests and the results suggested that the inherit risk of over-fitting of this type of approach is low.