Dataset Information

The Importance of Nonlinear Transformations Use in Medical Data Analysis.

ABSTRACT: BACKGROUND:The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. OBJECTIVE:Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. METHODS:We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer's Disease Neuroimaging Initiative study, Parkinson's disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. RESULTS:Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). CONCLUSIONS:This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results.

SUBMITTER: Shachar N

PROVIDER: S-EPMC5970282 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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The Importance of Nonlinear Transformations Use in Medical Data Analysis.

Shachar Netta N Mitelpunkt Alexis A Kozlovski Tal T Galili Tal T Frostig Tzviel T Brill Barak B Marcus-Kalish Mira M Benjamini Yoav Y

JMIR medical informatics 20180511 2

<h4>Background</h4>The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked.<h4>Objective</h4>Our aim is to present a semi-automated approach of ...[more]

PMID: 29752251

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Project description:BackgroundIn a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.ResultsWe compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.ConclusionsOur results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.

Project description:BackgroundThe SARS-CoV-2 pandemic has highlighted once more the great need for comprehensive access to, and uncomplicated use of, pre-existing patient data for medical research. Enabling secondary research-use of patient-data is a prerequisite for the efficient and sustainable promotion of translation and personalisation in medicine, and for the advancement of public-health. However, balancing the legitimate interests of scientists in broad and unrestricted data-access and the demand for individual autonomy, privacy and social justice is a great challenge for patient-based medical research.MethodsWe therefore conducted two questionnaire-based surveys among North-German outpatients (n = 650) to determine their attitude towards data-donation for medical research, implemented as an opt-out-process.ResultsWe observed a high level of acceptance (75.0%), the most powerful predictor of a positive attitude towards data-donation was the conviction that every citizen has a duty to contribute to the improvement of medical research (> 80% of participants approving data-donation). Interestingly, patients distinguished sharply between research inside and outside the EU, despite a general awareness that universities and public research institutions cooperate with commercial companies, willingness to allow use of donated data by the latter was very low (7.1% to 29.1%, depending upon location of company). The most popular measures among interviewees to counteract reservations against commercial data-use were regulation by law (61.4%), stipulating in the process that data are not sold or resold (84.6%). A majority requested control of both the use (46.8%) and the protection (41.5%) of the data by independent bodies.ConclusionsIn conclusion, data-donation for medical research, implemented as a combination of legal entitlement and easy-to-exercise-right to opt-out, was found to be widely supported by German patients and therefore warrants further consideration for a transposition into national law.

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The Importance of Nonlinear Transformations Use in Medical Data Analysis.

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The Importance of Nonlinear Transformations Use in Medical Data Analysis.

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