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Introduction: Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications. Objectives: Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health. Methods: We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC 2000 cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode. Results: The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state. Conclusion: The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications—when markers are present in both fasting and dynamic states. [ABSTRACT FROM AUTHOR]

This study deals with missing link prediction, the problem of predicting the existence of missing connections between entities of interest. We approach the problem as filling in missing entries in a relational dataset represented by several matrices and multiway arrays, that will be simply called tensors. Consequently, we address the link prediction problem by data fusion formulated as simultaneous factorization of several observation tensors where latent factors are shared among each observation. Previous studies on joint factorization of such heterogeneous datasets have focused on a single loss function (mainly squared Euclidean distance or Kullback-Leibler-divergence) and specific tensor factorization models (CANDECOMP/PARAFAC and/or Tucker). However, in this paper, we study various alternative tensor models as well as loss functions including the ones already studied in the literature using the generalized coupled tensor factorization framework. Through extensive experiments on two real-world datasets, we demonstrate that (i) joint analysis of data from multiple sources via coupled factorization significantly improves the link prediction performance, (ii) selection of a suitable loss function and a tensor factorization model is crucial for accurate missing link prediction and loss functions that have not been studied for link prediction before may outperform the commonly-used loss functions, (iii) joint factorization of datasets can handle difficult cases, such as the cold start problem that arises when a new entity enters the dataset, and (iv) our approach is scalable to large-scale data. [ABSTRACT FROM AUTHOR]

In metabolomics studies, liquid chromatography mass spectrometry (LC-MS) provides comprehensive information on biological samples. However, extraction of few relevant metabolites from this large and complex data is cumbersome. To resolve this issue, we have employed sparse principal component analysis (SPCA) to capture the underlying patterns and select relevant metabolites from LC-MS plasma profiles. The study involves a small pilot cohort with 270 subjects where each subject's time since last meal (TSLM) has been recorded prior to plasma sampling. Our results have demonstrated that both PCA and SPCA can capture the TSLM patterns. Nevertheless, SPCA provides more easily interpretable loadings in terms of selection of relevant metabolites, which are identified as amino acids and lyso-lipids. This study demonstrates the utility of SPCA as a pattern recognition and variable selection tool in metabolomics. Furthermore, amino acids and lyso-lipids are determined as dominating compounds in response to TSLM. [ABSTRACT FROM AUTHOR]