Identifying Significant Metabolic Pathways Using Multi-Block Partial Least-Squares Analysis

In metabolomics,
identification of metabolic pathways altered by
disease, genetics, or environmental perturbations is crucial to uncover
the underlying biological mechanisms. A number of pathway analysis
methods are currently available, which are generally based on equal-probability,
topological-centrality, or model-separability methods. In brief, prior
identification of significant metabolites is needed for the first
two types of methods, while each pathway is modeled separately in
the model-separability-based methods. In these methods, interactions
between metabolic pathways are not taken into consideration. The current
study aims to develop a novel metabolic pathway identification method
based on multi-block partial least squares (MB-PLS) analysis by including
all pathways into a global model to facilitate biological interpretation.
The detected metabolites are first assigned to pathway blocks based
on their roles in metabolism as defined by the KEGG pathway database.
The metabolite intensity or concentration data matrix is then reconstructed
as data blocks according to the metabolite subsets. Then, a MB-PLS
model is built on these data blocks. A new metric, named the pathway
importance in projection (PIP), is proposed for evaluation of the
significance of each metabolic pathway for group separation. A simulated
dataset was generated by imposing artificial perturbation on four
pre-defined pathways of the healthy control group of a colorectal
cancer study. Performance of the proposed method was evaluated and
compared with seven other commonly used methods using both an actual
metabolomics dataset and the simulated dataset. For the real metabolomics
dataset, most of the significant pathways identified by the proposed
method were found to be consistent with the published literature.
For the simulated dataset, the significant pathways identified by
the proposed method are highly consistent with the pre-defined pathways.
The experimental results demonstrate that the proposed method is effective
for identification of significant metabolic pathways, which may facilitate
biological interpretation of metabolomics data.