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      Reproducible analysis of disease space via principal components using the novel R package syndRomics

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          Abstract

          Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. ‘Syndromics’ refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.

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          Most cited references 75

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

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            Missing data: our view of the state of the art.

            Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.
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              mice: Multivariate Imputation by Chained Equations inR

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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                14 January 2021
                2021
                : 10
                Affiliations
                [1 ]Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF) San FranciscoUnited States
                [2 ]Department of Neurological Surgery, University of California San Francisco (UCSF) San FranciscoUnited States
                [3 ]Zuckerberg San Francisco General Hospital and Trauma Center San FranciscoUnited States
                [4 ]School of Medicine, University of California San Diego (UCSD) San DiegoUnited States
                [5 ]San Francisco VA Health Care System San FranciscoUnited States
                Icahn School of Medicine at Mount Sinai United States
                University of Zurich Switzerland
                Icahn School of Medicine at Mount Sinai United States
                Article
                61812
                10.7554/eLife.61812
                7857733
                33443012
                b6da084e-4d57-46d9-a136-5d9fc245b68a
                © 2021, Torres-Espín et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                Product
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NS106899
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: NS088475
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000738, Department of Veterans Affairs;
                Award ID: I01RX02245
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000738, Department of Veterans Affairs;
                Award ID: I01RX002787
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005191, Craig H. Neilsen Foundation;
                Award ID: Special Project
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008191, Wings for Life;
                Award ID: Special Project
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008191, Wings for Life;
                Award ID: Individual Grant
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Computational and Systems Biology
                Medicine
                Custom metadata
                A tutorial and open-source software to aid in reproducible disease pattern detection using principal component analysis.

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