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      mixOmics: An R package for ‘omics feature selection and multiple data integration

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          Abstract

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.

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          Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.

          Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.
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            Dimension reduction techniques for the integrative analysis of multi-omics data

            State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput ‘omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.
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              Erratum to: Clinical metagenomic identification of Balamuthia mandrillaris encephalitis and assembly of the draft genome: the continuing case for reference genome sequencing

              Erratum In the original article [1], there was an error in one of the author names. The author name Bette K. Kleinschmidt-DeMasters was incorrectly spelt as Bette K. Klenschmidt-DeMasters. This has now been corrected in the published article and the publisher apologises for any inconvenience caused.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: SoftwareRole: Visualization
                Role: InvestigationRole: MethodologyRole: Visualization
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                November 2017
                3 November 2017
                : 13
                : 11
                : e1005752
                Affiliations
                [1 ] The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
                [2 ] Prevention of Organ Failure (PROOF) Centre of Excellence, Vancouver, British Columbia, Canada
                [3 ] Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
                [4 ] Melbourne Integrative Genomics and School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia
                Hebrew University of Jerusalem, ISRAEL
                Author notes

                The authors declare that they have no competing interests.

                [¤]

                Current address: Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia

                Author information
                http://orcid.org/0000-0002-9588-8000
                http://orcid.org/0000-0003-3923-1116
                Article
                PCOMPBIOL-D-17-00712
                10.1371/journal.pcbi.1005752
                5687754
                29099853
                5c2485d2-1a7a-4012-8602-1fd1803c846a
                © 2017 Rohart et al

                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
                : 5 May 2017
                : 31 August 2017
                Page count
                Figures: 5, Tables: 2, Pages: 19
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: APP1087415
                Award Recipient :
                Funded by: Australian Cancer Research Foundation (ACRF) for the Diamantina Individualised Oncology Care Centre
                Award Recipient :
                FR was supported, in part, by the Australian Cancer Research Foundation (ACRF) for the Diamantina Individualised Oncology Care Centre at The University of Queensland Diamantina Institute. KALC was supported, in part, by the National Health and Medical Research Council (NHMRC) Career Development fellowship (APP1087415). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer and Information Sciences
                Data Visualization
                Computer and Information Sciences
                Information Technology
                Data Mining
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Proteomic Databases
                Biology and Life Sciences
                Biochemistry
                Proteomics
                Proteomic Databases
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Biology and life sciences
                Genetics
                Gene expression
                Gene regulation
                MicroRNAs
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Non-coding RNA
                MicroRNAs
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Multivariate Analysis
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-11-15
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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