Analysis of multi-source metabolomic data using joint and individual variation explained (JIVE)

Abstract

Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discovery and reliable biomarker identification. Another field of application of data fusion is the simultaneous analysis of metabolomic changes through several biofluids or tissues. However, metabolomics typically deals with large datasets, with hundreds to thousands of variables and the identification of shared and individual factors or structures across multiple sources is challenging due to the high variable to sample ratios and differences in intensity and noise range. In this work we apply a recent method, Joint and Individual Variation Explained (JIVE), for the integrated unsupervised analysis of metabolomic profiles from multiple data sources. This method separates the shared patterns among data sources (i.e. the joint structure) from the individual structure of each data source that is unrelated to the joint structure. Two examples are described to show the applicability of JIVE for the simultaneous analysis of multi-source data using: (i) plasma samples subjected to different analytical techniques, sample treatment and measurement conditions; and (ii) plasma and urine samples subjected to liquid chromatography-mass spectrometry measured using two ionization conditions.

Performance of methods that separate common and distinct variation in multiple data blocks

“…The JIVE method has gained popularity mainly in biomedical applications . The JIVE is, similar to DISCO, also an extension of the regular SCA decomposition and can be used for both multiblock and multiset applications.…”

Multimodal vibrational studies of drug uptake in vitro: Is the whole greater than the sum of their parts?

“…On the other hand, specific markers of DOX [34], such as the bands found at 1283 cm −1 (δO‐H …O) and 990 cm −1 (δC─OH) were not evident. These bands are the most intense in the IR spectrum of DOX [29], and are in a region where other cell components do not show high absorbances. The lack of spectral markers associated to these bands evidences a negligible contribution of DOX bands to the IR spectra, probably because of the low concentration of DOX within the cell (i.e.…”

“…Hence, the combination of Raman and IR variables becomes a challenge. Different methods have been applied to integrate data from IR, Raman and other metabolic techniques, including PCA [24,25], 2D correlation analysis [26,27], MCR-ALS [28] and joint individual variation analysis [29]. In the context of a drug uptake study, the analysis becomes even more complex when the incubation time variable is introduced in the system.…”

Angle-based joint and individual variation explained

“…Integration of these is fundamental for studying cancer on a molecular level. Other types of application include analysis of multi-source metabolomic data [15], extraction of commuting patterns in railway networks [10], recognition of brain-computer interface [49], etc.…”