{"data":{"id":"10.5061/dryad.9w0vt4bf5","type":"dois","attributes":{"doi":"10.5061/dryad.9w0vt4bf5","prefix":"10.5061","suffix":"dryad.9w0vt4bf5","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Lin, Ye","nameType":"Personal","affiliation":["Boston University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2506-189X","nameIdentifierScheme":"ORCID"}]},{"name":"Andersson, Sean B.","nameType":"Personal","affiliation":["Boston University"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7575-3507","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images - Simulation Data"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"FOS: Nano-technology","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"}],"contributors":[{"name":"Lin, Ye","nameType":"Personal","affiliation":["Boston University"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2506-189X","nameIdentifierScheme":"ORCID"}]},{"name":"Andersson, Sean B.","nameType":"Personal","affiliation":["Boston University"],"contributorType":"ContactPerson","nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7575-3507","nameIdentifierScheme":"ORCID"}]}],"dates":[{"date":"2021-03-19T14:42:03Z","dateType":"Created"},{"date":"2021-05-05T14:40:03Z","dateType":"Submitted"},{"date":"2021-05-17T00:00:00Z","dateType":"Issued"},{"date":"2021-05-17T00:00:00Z","dateType":"Available"},{"date":"2021-05-26T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0243115","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["172943393 bytes"],"formats":[],"version":"9","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The datasets contain simulated Single Particle Tracking\n (SPT) data consisting of sequences of camera images of a single\n fluorescent sub-diffraction limit-sized particle undergoing\n two-dimensional diffusion. We simulated a variety of experimental\n conditions, including different Signal-to-Background Ratios (SBRs), two\n different camera types, different diffusion speeds, and two different\n settings for motion blur. SPT is a class of experimental methods and data\n analysis techniques for exploring the motion of individual biological\n macromolecules. Typical estimation algorithms split the problem into two\n parts: first localize the particle at each data point to generate a\n trajectory and then estimate model parameters from that trajectory. We\n have recently introduced a class of algorithms for jointly estimating both\n trajectory and model parameters. In this study, we used the data to\n perform quantitative comparisons between two variants of our approach, one\n relying on a Sequential Monte Carlo methods combined with\n Expectation Maximization (SMC-EM) that is applicable to a very broad set\n of motion and observation models, and one that replaces the SMC elements\n with methods based on the Unscented Kalman Filter (UKF) to improve upon\n the computational complexity. We also compared our methods to two current\n standards in the field. The first uses Gaussian Fitting to localize the\n particle, following by a Mean Square Displacement (GF-MSD) analysis to\n determine model parameters while the other replaces MSD with\n Maximum Likelihood Estimation (GF-MLE). The main results of our study\n indicate that our EM-based schemes significantly outperform the existing\n algorithms at low SBR while at high SBR, GF-MLE performs equally well but\n at a lower computational cost.","descriptionType":"Abstract"},{"description":"All datasets were simulated using MATLAB (MathWorks, Natick, MA).\n Two camera types were considered: (1) An ideal camera with Poisson\n distributed shot noise but no readout noise; (2) A camera with both shot\n noise and the pixel-dependent readout noise that is common to scientific\n complementary metal-oxide semiconductor (sCMOS) cameras. For each run, the\n diffusive motion of a single particle was generated at a time step of 1\n ms. At each time step, a pixelated image was generated by integrating a\n standard Gaussian model of the Point Spread Function (PSF) of the\n microscope over each of the pixels in the camera image. The cameras were\n assumed to take images at a rate of 10 Hz with a shutter period of 10 ms.\n For data that included motion blur, the 10 pixelated images in the shutter\n period were accumulated to generate a single camera image. For data that\n ignored motion blur, only the first pixelated image was used. In each\n case, both background and camera readout noise were included based on the\n model of the camera being considered. Datasets were generated at a range\n of SBRs and diffusion coefficients (see Usage Notes for\n details).","descriptionType":"Methods"},{"description":"* DATA-SPECIFIC INFORMATION FOR:\n [type1_data_with_different_N_G.zip] Images\n were captured by the camera with Poisson distributed shot noise but no\n readout noise. The background noise ranges from 1 to\n 15, while the signal level ranges from 1 to 100.\n Motion blur is considered (each image contains 100 sub-samples,\n and only the first 10 sub-samples of every 100 sub-samples were\n accumulated). Taking the sub-directory\n [N10G100Image100] as an example, the dataset name indicates the following\n parameter values: background noise=10, signal intensity=100, number of\n images per dataset=100. Under each experimental setting, 100 datasets were\n simulated. Taking the first dataset under\n [N10G100Image100] as an example, the details of the provided data are as\n follows: [photon_observation_1.csv]\n row*column=100*25; There are the simulated images with each row containing\n a single image, organized sequentially from the initial time down to the\n final time. The 25 entries in each row are the intensity values from the\n 5-by-5 pixel array, organized starting from the top-left pixel, going\n horizontally across the top five pixels, and continuing to the bottom\n right pixel.\n [sensor_position_1.csv] row*column=100*2; In generating images,\n we assumed segmentation of the full camera image was done previously and\n thus the location of the 5-by-5 array of pixels may change at each time\n step. This data gives the position of the pixel array starting from the\n initial time down to the final time, with the first column corresponding\n to the x-coordinate and the second column to the y-coordinate.\n [x_ground_truth.mat] row*column=100*100; This data contains the\n true location of the particle in the x-direction at each time step.\n Because of the motion blur model, this location was taken to be the mean\n of the position across the shutter period. The data is organized starting\n with the first time step and proceeding down to the last.\n [y_ground_truth.mat] row*column=100*100; This data is the true\n location of the particle in the y-direction at each time step, and it is\n organized as [x_ground_truth.mat]. \n * DATA-SPECIFIC INFORMATION FOR:\n [type1_N10G100Image100D_different.zip] \n Images were captured by the camera with Poisson distributed shot\n noise but no readout noise. Background noise=10,\n signal intensity=100, number of images per dataset =100, diffusion\n coefficient ranges from 0.001 to 10. The\n sub-directory [100datasets_withSub] contains the data with motion blur,\n while the sub-directory [100datasets_noSub] contains the data without\n motion blur. Taking the files\n in [100datasets_withSub/N10G100D0.01] as an example, the diffusion\n coefficient is Dx=Dy=0.01(um\u003csup\u003e2\u003c/sup\u003e/s). The variable\n names and their corresponding dimensions are the same as the descriptions\n of [type1_data_with_different_N_G.zip]. *\n DATA-SPECIFIC INFORMATION FOR: [type2_camera.zip] \n Images were captured by the camera with both shot noise and the\n pixel-dependent readout noise. [N10G100Image100sCMOS]\n background noise=10, signal intensity=100, number of images per\n dataset=100. For each experimental setting, 100 datasets were simulated.\n Taking the 1st dataset as an example, the provided data is:  \n [local_sigma_1.csv] row*column=100*25; row denotes the time\n steps, column denotes the pixel. Each pixel contains information of\n \"sigma\" characterizing the readout noise of the Hamamatsu ORCA\n Flash 4.0 camera. [photon_observation_1.csv] are the\n measured intensities, organized as described above.\n [sensor_position_1.csv] are the global locations of the 5-by-5\n pixel arrays, organized as described above.\n [x_ground_truth.mat] are the ground truth in the x-direction,\n organized as described above. \n  [N10G30Image100sCMOS] background noise=10, signal intensity=30, number of images per dataset=100. For each experimental setting, 100 datasets were simulated. This data is organized in the same manner as described above. * DATA-SPECIFIC INFORMATION FOR: [Data_for_figures_PlosOne.zip] Figures and their corresponding data used for reproducing the figures in paper “Ye Lin and Sean B. Andersson. Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images. Plos One (2021).”","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"awardUri":"https://reporter.nih.gov/project-details/9308101","awardTitle":"\n        Joint estimation of motion model, model parameters, and particle\n        trajectories in single particle tracking\n      ","funderName":"National Institute of General Medical Sciences","awardNumber":"1R01GM117039-01A1","funderIdentifier":"https://ror.org/04q48ey07","funderIdentifierType":"ROR"}],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.9W0VT4BF5</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Lin, Ye</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2506-189X</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/05qwgg493" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Boston University</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Andersson, Sean B.</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7575-3507</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/05qwgg493" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Boston University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>
      Expectation maximization based framework for joint localization and
      parameter estimation in single particle tracking from segmented images -
      Simulation Data
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject subjectScheme="fos" schemeURI="https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf">FOS: Nano-technology</subject>
  </subjects>
  <fundingReferences>
    <fundingReference>
      <funderName>National Institute of General Medical Sciences</funderName>
      <funderIdentifier funderIdentifierType="ROR">https://ror.org/04q48ey07</funderIdentifier>
      <awardNumber awardURI="https://reporter.nih.gov/project-details/9308101">1R01GM117039-01A1</awardNumber>
      <awardTitle>
        Joint estimation of motion model, model parameters, and particle
        trajectories in single particle tracking
      </awardTitle>
    </fundingReference>
  </fundingReferences>
  <contributors>
    <contributor contributorType="ContactPerson">
      <contributorName nameType="Personal">Lin, Ye</contributorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2506-189X</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/05qwgg493" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Boston University</affiliation>
    </contributor>
    <contributor contributorType="ContactPerson">
      <contributorName nameType="Personal">Andersson, Sean B.</contributorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7575-3507</nameIdentifier>
      <affiliation affiliationIdentifier="https://ror.org/05qwgg493" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">Boston University</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Created">2021-03-19T14:42:03Z</date>
    <date dateType="Submitted">2021-05-05T14:40:03Z</date>
    <date dateType="Issued">2021-05-17T00:00:00Z</date>
    <date dateType="Available">2021-05-17T00:00:00Z</date>
    <date dateType="Updated">2021-05-26T00:00:00Z</date>
  </dates>
  <language>en</language>
  <relatedIdentifiers>
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  <descriptions>
    <description descriptionType="Abstract">
      The&amp;nbsp;datasets contain&amp;nbsp;simulated Single Particle Tracking
      (SPT) data consisting of sequences of camera images of a single
      fluorescent sub-diffraction limit-sized particle undergoing
      two-dimensional diffusion. We simulated a variety of experimental
      conditions, including different Signal-to-Background Ratios (SBRs), two
      different camera types, different diffusion speeds, and two different
      settings for motion blur. SPT is a class of experimental methods and data
      analysis techniques for exploring the motion of individual biological
      macromolecules. Typical estimation algorithms split the problem into two
      parts: first localize the particle at each data point to generate a
      trajectory and then estimate model parameters from that trajectory. We
      have recently introduced a class of algorithms for jointly estimating both
      trajectory and model parameters. In this study, we used the data to
      perform quantitative comparisons between two variants of our approach, one
      relying on a&amp;nbsp;Sequential Monte Carlo methods combined with
      Expectation Maximization (SMC-EM) that is applicable to a very broad set
      of motion and observation models, and one that replaces the SMC elements
      with methods based on the Unscented Kalman Filter (UKF) to improve upon
      the computational complexity. We also compared our methods to two current
      standards in the field. The first uses Gaussian Fitting to localize the
      particle, following by a Mean Square Displacement (GF-MSD) analysis to
      determine model parameters&amp;nbsp;while the other replaces MSD with
      Maximum Likelihood Estimation (GF-MLE). The main results of our study
      indicate that our EM-based schemes significantly outperform the existing
      algorithms at low SBR while at high SBR, GF-MLE performs equally well but
      at a lower computational cost.
    </description>
    <description descriptionType="Methods">
      &lt;p&gt;All datasets were simulated using MATLAB (MathWorks, Natick, MA).
      Two camera types were considered: (1) An ideal camera with Poisson
      distributed shot noise but no readout noise; (2) A camera with both shot
      noise and the pixel-dependent readout noise that is common to scientific
      complementary metal-oxide semiconductor (sCMOS) cameras. For each run, the
      diffusive motion of a single particle was generated at a time step of 1
      ms. At each time step, a pixelated image was generated by integrating a
      standard Gaussian model of the Point Spread Function (PSF) of the
      microscope over each of the pixels in the camera image. The cameras were
      assumed to take images at a rate of 10 Hz with a shutter period of 10 ms.
      For data that included motion blur, the 10 pixelated images in the shutter
      period were accumulated to generate a single camera image. For data that
      ignored motion blur, only the first pixelated image was used. In each
      case, both background and camera readout noise were included based on the
      model of the camera being considered. Datasets were generated at a range
      of SBRs and diffusion coefficients (see Usage Notes for
      details).&lt;/p&gt;
    </description>
    <description descriptionType="Other">
      &lt;p&gt;* DATA-SPECIFIC INFORMATION FOR:
      [type1_data_with_different_N_G.zip]&lt;/p&gt; &lt;ol&gt; &lt;li&gt;Images
      were captured by the camera with Poisson distributed shot noise but no
      readout noise.&lt;/li&gt; &lt;li&gt;The background noise ranges from 1 to
      15, while the signal level ranges from 1 to 100.&lt;/li&gt;
      &lt;li&gt;Motion blur is considered (each image contains 100 sub-samples,
      and only the first 10 sub-samples of every 100 sub-samples were
      accumulated).&lt;/li&gt; &lt;li&gt;Taking the sub-directory
      [N10G100Image100] as an example, the dataset name indicates the following
      parameter values: background noise=10, signal intensity=100, number of
      images per dataset=100. Under each experimental setting, 100 datasets were
      simulated.&lt;/li&gt; &lt;li&gt;Taking the first dataset under
      [N10G100Image100] as an example, the details of the provided data are as
      follows: &lt;ul&gt; &lt;li&gt;[photon_observation_1.csv]
      row*column=100*25; There are the simulated images with each row containing
      a single image, organized sequentially from the initial time down to the
      final time. The 25 entries in each row are the intensity values from the
      5-by-5 pixel array, organized starting from the top-left pixel, going
      horizontally across the top five pixels, and continuing to the bottom
      right pixel.&lt;/li&gt;
      &lt;li&gt;[sensor_position_1.csv] row*column=100*2; In generating images,
      we assumed segmentation of the full camera image was done previously and
      thus the location of the 5-by-5 array of pixels may change at each time
      step. This data gives the position of the pixel array starting from the
      initial time down to the final time, with the first column corresponding
      to the x-coordinate and the second column to the y-coordinate.&lt;/li&gt;
      &lt;li&gt;[x_ground_truth.mat] row*column=100*100; This data contains the
      true location of the particle in the x-direction at each time step.
      Because of the motion blur model, this location was taken to be the mean
      of the position across the shutter period. The data is organized starting
      with the first time step and proceeding down to the last.&lt;/li&gt;
      &lt;li&gt;[y_ground_truth.mat] row*column=100*100; This data is the true
      location of the particle in the y-direction at each time step, and it is
      organized as [x_ground_truth.mat].&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt;
      &lt;/ol&gt; &lt;p&gt;* DATA-SPECIFIC INFORMATION FOR:
      [type1_N10G100Image100D_different.zip]&lt;/p&gt; &lt;ol&gt;
      &lt;li&gt;Images were captured by the camera with Poisson distributed shot
      noise but no readout noise.&lt;/li&gt; &lt;li&gt;Background noise=10,
      signal intensity=100, number of images per dataset =100, diffusion
      coefficient ranges from 0.001 to 10.&lt;/li&gt; &lt;li&gt;The
      sub-directory [100datasets_withSub] contains the data with motion blur,
      while the sub-directory [100datasets_noSub] contains the data without
      motion blur.&lt;/li&gt; &lt;li&gt;Taking the files
      in [100datasets_withSub/N10G100D0.01] as an example, the diffusion
      coefficient is Dx=Dy=0.01(um&lt;sup&gt;2&lt;/sup&gt;/s). The variable
      names and their corresponding dimensions are the same as the descriptions
      of [type1_data_with_different_N_G.zip].&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;*
      DATA-SPECIFIC INFORMATION FOR: [type2_camera.zip]&lt;/p&gt; &lt;ol&gt;
      &lt;li&gt;Images were captured by the camera with both shot noise and the
      pixel-dependent readout noise.&lt;/li&gt; &lt;li&gt;[N10G100Image100sCMOS]
      background noise=10, signal intensity=100, number of images per
      dataset=100. For each experimental setting, 100 datasets were simulated.
      Taking the 1st dataset as an example, the provided data is:  &lt;ul&gt;
      &lt;li&gt;[local_sigma_1.csv] row*column=100*25; row denotes the time
      steps, column denotes the pixel. Each pixel contains information of
      "sigma" characterizing the readout noise of the Hamamatsu ORCA
      Flash 4.0 camera.&lt;/li&gt; &lt;li&gt;[photon_observation_1.csv] are the
      measured intensities, organized as described above.&lt;/li&gt;
      &lt;li&gt;[sensor_position_1.csv] are the global locations of the 5-by-5
      pixel arrays, organized as described above.&lt;/li&gt;
      &lt;li&gt;[x_ground_truth.mat] are the ground truth in the x-direction,
      organized as described above.&lt;/li&gt; &lt;/ul&gt; &lt;/li&gt;
      &lt;li&gt; &lt;span
      style="display:none;"&gt; &lt;/span&gt;[N10G30Image100sCMOS] background noise=10, signal intensity=30, number of images per dataset=100. For each experimental setting, 100 datasets were simulated. This data is organized in the same manner as described above.&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;* DATA-SPECIFIC INFORMATION FOR: [Data_for_figures_PlosOne.zip]&lt;/p&gt; &lt;p style="margin-left:40px;"&gt;&lt;span&gt;Figures and their corresponding data used for reproducing the figures in paper “Ye Lin and Sean B. Andersson. Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images. Plos One (2021).”&lt;/span&gt;&lt;/p&gt;
    </description>
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