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      Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

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      PLoS ONE
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          Abstract

          In the current era of big data, the amount of data available is continuously increasing. Both the number and types of samples, or features, are on the rise. The mixing of distinct features often makes interpretation more difficult. However, separate analysis of individual types requires subsequent integration. A tensor is a useful framework to deal with distinct types of features in an integrated manner without mixing them. On the other hand, tensor data is not easy to obtain since it requires the measurements of huge numbers of combinations of distinct features; if there are m kinds of features, each of which has N dimensions, the number of measurements needed are as many as N m , which is often too large to measure. In this paper, I propose a new method where a tensor is generated from individual features without combinatorial measurements, and the generated tensor was decomposed back to matrices, by which unsupervised feature extraction was performed. In order to demonstrate the usefulness of the proposed strategy, it was applied to synthetic data, as well as three omics datasets. It outperformed other matrix-based methodologies.

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          Most cited references63

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            In silico prediction of protein-protein interactions in human macrophages

            Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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              MicroRNA sequence and expression analysis in breast tumors by deep sequencing.

              MicroRNAs (miRNA) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 noninvasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed sequence-dependent miRNA target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most noninvasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including miR-98/let-7), with most miRNA changes apparent already in the noninvasive carcinomas. In addition, patients that went on to develop metastasis showed increased expression of mir-423, and triple-negative breast carcinomas were most distinct from other tumor subtypes due to upregulation of the mir~17-92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested. ©2011 AACR.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                25 August 2017
                : 12
                : 8
                : e0183933
                Affiliations
                [001] Department of Physics, Chuo University, 1-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
                Nanjing Normal University, CHINA
                Author notes

                Competing Interests: The author has declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0867-8986
                Article
                PONE-D-17-06844
                10.1371/journal.pone.0183933
                5571984
                28841719
                cf178766-ff8e-452f-a9c4-48c794c333fb
                © 2017 Y-h. Taguchi

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 February 2017
                : 4 August 2017
                Page count
                Figures: 9, Tables: 6, Pages: 36
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: 17K00417
                Award Recipient :
                This work was supported by Japan Society for the Promotion of Science-17K00417-Prof. Y-h. Taguchi.
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                All data sets from GEO GEO ID: GSE28884, GSE84096, GSE18323.

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