A theorem proving approach for automatically synthesizing visualizations of flow cytometry data View Full Text


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Article Info

DATE

2017-06

AUTHORS

Sunny Raj, Faraz Hussain, Zubir Husein, Neslisah Torosdagli, Damla Turgut, Narsingh Deo, Sumanta Pattanaik, Chung-Che (Jeff) Chang, Sumit Kumar Jha

ABSTRACT

BACKGROUND: Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset. RESULTS: This paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections. CONCLUSIONS: We describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data. More... »

PAGES

245

References to SciGraph publications

  • 2017-11. A theory of formal synthesis via inductive learning in ACTA INFORMATICA
  • 2003-01. Beyond six colors: A new era in flow cytometry in NATURE MEDICINE
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  • 2013. Emerging Paradigms in Machine Learning in NONE
  • 2008. Z3: An Efficient SMT Solver in TOOLS AND ALGORITHMS FOR THE CONSTRUCTION AND ANALYSIS OF SYSTEMS
  • 2011-07. Next-generation flow cytometry in NATURE BIOTECHNOLOGY
  • 2009. Boolector: An Efficient SMT Solver for Bit-Vectors and Arrays in TOOLS AND ALGORITHMS FOR THE CONSTRUCTION AND ANALYSIS OF SYSTEMS
  • 2004-08. Seventeen-colour flow cytometry: unravelling the immune system in NATURE REVIEWS IMMUNOLOGY
  • 2009. Beaver: Engineering an Efficient SMT Solver for Bit-Vector Arithmetic in COMPUTER AIDED VERIFICATION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12859-017-1662-4

    DOI

    http://dx.doi.org/10.1186/s12859-017-1662-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1085908457

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/28617220


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        "description": "BACKGROUND: Polychromatic flow cytometry is a popular technique that has wide usage in the medical sciences, especially for studying phenotypic properties of cells. The high-dimensionality of data generated by flow cytometry usually makes it difficult to visualize. The naive solution of simply plotting two-dimensional graphs for every combination of observables becomes impractical as the number of dimensions increases. A natural solution is to project the data from the original high dimensional space to a lower dimensional space while approximately preserving the overall relationship between the data points. The expert can then easily visualize and analyze this low-dimensional embedding of the original dataset.\nRESULTS: This paper describes a new method, SANJAY, for visualizing high-dimensional flow cytometry datasets. This technique uses a decision procedure to automatically synthesize two-dimensional and three-dimensional projections of the original high-dimensional data while trying to minimize distortion. We compare SANJAY to the popular multidimensional scaling (MDS) approach for visualization of small data sets drawn from a representative set of benchmarks, and our experiments show that SANJAY produces distortions that are 1.44 to 4.15 times smaller than those caused due to MDS. Our experimental results show that SANJAY also outperforms the Random Projections technique in terms of the distortions in the projections.\nCONCLUSIONS: We describe a new algorithmic technique that uses a symbolic decision procedure to automatically synthesize low-dimensional projections of flow cytometry data that typically have a high number of dimensions. Our algorithm is the first application, to our knowledge, of using automated theorem proving for automatically generating highly-accurate, low-dimensional visualizations of high-dimensional data.", 
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