Structure-revealing data fusion

BACKGROUND: Analysis of data from multiple sources has the potential to enhance knowledge discovery by capturing underlying structures, which are, otherwise, difficult to extract. Fusing data from multiple sources has already proved useful in many applications in social network analysis, signal processing and bioinformatics. However, data fusion is challenging since data from multiple sources are often (i) heterogeneous (i.e., in the form of higher-order tensors and matrices), (ii) incomplete, and (iii) have both shared and unshared components. In order to address these challenges, in this paper, we introduce a novel unsupervised data fusion model based on joint factorization of matrices and higher-order tensors. RESULTS: While the traditional formulation of coupled matrix and tensor factorizations modeling only shared factors fails to capture the underlying structures in the presence of both shared and unshared factors, the proposed data fusion model has the potential to automatically reveal shared and unshared components through modeling constraints. Using numerical experiments, we demonstrate the effectiveness of the proposed approach in terms of identifying shared and unshared components. Furthermore, we measure a set of mixtures with known chemical composition using both LC-MS (Liquid Chromatography - Mass Spectrometry) and NMR (Nuclear Magnetic Resonance) and demonstrate that the structure-revealing data fusion model can (i) successfully capture the chemicals in the mixtures and extract the relative concentrations of the chemicals accurately, (ii) provide promising results in terms of identifying shared and unshared chemicals, and (iii) reveal the relevant patterns in LC-MS by coupling with the diffusion NMR data. CONCLUSIONS: We have proposed a structure-revealing data fusion model that can jointly analyze heterogeneous, incomplete data sets with shared and unshared components and demonstrated its promising performance as well as potential limitations on both simulated and real data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-239) contains supplementary material, which is available to authorized users

Fluorescence spectroscopy as a potential metabonomic tool for early detection of colorectal cancer

Abstract Fluorescence spectroscopy Excitation Emission Matrix (EEM) measurements were applied on human blood plasma samples from a case control study on colorectal cancer. Samples were collected before large bowel endoscopy and included patients with colorectal cancer or with adenomas, and from individuals with other non malignant findings or no findings (N = 308). The objective of the study was to explore the possibilities for applying fluorescence spectroscopy as a tool for detection of colorectal cancer. Parallel Factor Analysis (PARAFAC) was applied to decompose the fluorescence EEMs into estimates of the underlying fluorophores in the sample. Both the pooled score matrix from PARAFAC, holding the relative concentrations of the derived components, and the raw unfolded spectra were used as basis for discrimination models between cancer and the various controls. Both methods gave test set validated sensitivity and specificity values around 0.75 between cancer and controls, and poor discriminations between the various controls. The PARA-FAC solution gave better options for analyzing the chemical mechanisms behind the discrimination, and revealed a blue shift in tryptophan emission in the cancer patients, a result that supports previous findings. The present findings show how fluorescence spectroscopy and chemometrics can help in cancer diagnostics, and with PARAFAC fluorescence spectroscopy can be a potential metabonomic tool