The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform

Teppei Shimamura - One of the best experts on this subject based on the ideXlab platform.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    BMC Bioinformatics, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    bioRxiv, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Abstract Motivation Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. Results We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Conclusions Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.

Kodai Minoura - One of the best experts on this subject based on the ideXlab platform.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    BMC Bioinformatics, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    bioRxiv, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Abstract Motivation Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. Results We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Conclusions Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.

John R. Grace - One of the best experts on this subject based on the ideXlab platform.

  • monitoring electrostatics and hydrodynamics in gas solid bubbling fluidized beds using novel electrostatic probes
    Industrial & Engineering Chemistry Research, 2015
    Co-Authors: John R. Grace
    Abstract:

    The ability of recently developed novel electrostatic probes to monitor particle charge density and hydrodynamics in freely bubbling two- and three-dimensional fluidized beds of glass beads and polyethylene particles is demonstrated. Particle charge density and bubble properties in the beds were altered by abruptly changing superficial gas velocity or by impulsively adding antistatic agent to the bed. The probes were then utilized to quantitatively monitor the particle charge density and bubble rise velocity. The current signals from the probes responded quickly and significantly to abrupt changes in the superficial gas velocity. By Analyzing Time-Series signals from the probes, the particle charge density and the bubble rise velocity deduced from the probes were found to be of similar order of magnitudes and changed consistently with those obtained from Faraday cup and video measurements. Charge densities from the Faraday cup decreased when an antistatic agent was added, as registered by the probe.

  • Monitoring Electrostatics and Hydrodynamics in Gas–Solid Bubbling Fluidized Beds Using Novel Electrostatic Probes
    2015
    Co-Authors: John R. Grace
    Abstract:

    The ability of recently developed novel electrostatic probes to monitor particle charge density and hydrodynamics in freely bubbling two- and three-dimensional fluidized beds of glass beads and polyethylene particles is demonstrated. Particle charge density and bubble properties in the beds were altered by abruptly changing superficial gas velocity or by impulsively adding antistatic agent to the bed. The probes were then utilized to quantitatively monitor the particle charge density and bubble rise velocity. The current signals from the probes responded quickly and significantly to abrupt changes in the superficial gas velocity. By Analyzing Time-Series signals from the probes, the particle charge density and the bubble rise velocity deduced from the probes were found to be of similar order of magnitudes and changed consistently with those obtained from Faraday cup and video measurements. Charge densities from the Faraday cup decreased when an antistatic agent was added, as registered by the probe

Hiroyoshi Nishikawa - One of the best experts on this subject based on the ideXlab platform.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    BMC Bioinformatics, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    bioRxiv, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Abstract Motivation Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. Results We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Conclusions Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.

Yuka Maeda - One of the best experts on this subject based on the ideXlab platform.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    BMC Bioinformatics, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.

  • model based cell clustering and population tracking for Time Series flow cytometry data
    bioRxiv, 2019
    Co-Authors: Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura
    Abstract:

    Abstract Motivation Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on “manual gating” by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for Time-Series flow cytometry data. Time-Series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static Time-point analysis. Therefore, there is a great need for tools to systematically analyze Time-Series flow cytometry data. Results We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for Time-Series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current Time dependent on that at a previous Timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking Time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. Conclusions Our results indicate that CYBERTRACK offers better understandings of Time-dependent cell population dynamics to cytometry users by systematically Analyzing Time-Series flow cytometry data.