### Abstract

We introduce a nearly automatic procedure to locate and count the quantum dots in images of kinesin motor assays. Our procedure employs an approximate likelihood estimator based on a two-component mixture model for the image data; the first component has a normal distribution, and the other component is distributed as a normal random variable plus an exponential random variable. The normal component has an unknown variance, which we model as a function of the mean. We use B-splines to estimate the variance function during a training run on a suitable image, and the estimate is used to process subsequent images. Parameter estimates are generated for each image along with estimates of standard errors, and the number of dots in the image is determined using an information criterion and likelihood ratio tests. Realistic simulations show that our procedure is robust and that it leads to accurate estimates, both of parameters and of standard errors.

Original language | English (US) |
---|---|

Pages (from-to) | 588-595 |

Number of pages | 8 |

Journal | Biometrics |

Volume | 67 |

Issue number | 2 |

DOIs | |

State | Published - Jun 2011 |

Externally published | Yes |

### Fingerprint

### Keywords

- Bio-imaging
- Fluorescence microscopy
- Kinesin motor protein
- Maximum likelihood
- Mixture model
- Quantum dot
- Spline
- Variance-function estimation

### ASJC Scopus subject areas

- Applied Mathematics
- Statistics and Probability
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Medicine(all)

### Cite this

*Biometrics*,

*67*(2), 588-595. https://doi.org/10.1111/j.1541-0420.2010.01467.x

**A Mixture Model for Quantum Dot Images of Kinesin Motor Assays.** / Hughes, John; Fricks, John.

Research output: Contribution to journal › Article

*Biometrics*, vol. 67, no. 2, pp. 588-595. https://doi.org/10.1111/j.1541-0420.2010.01467.x

}

TY - JOUR

T1 - A Mixture Model for Quantum Dot Images of Kinesin Motor Assays

AU - Hughes, John

AU - Fricks, John

PY - 2011/6

Y1 - 2011/6

N2 - We introduce a nearly automatic procedure to locate and count the quantum dots in images of kinesin motor assays. Our procedure employs an approximate likelihood estimator based on a two-component mixture model for the image data; the first component has a normal distribution, and the other component is distributed as a normal random variable plus an exponential random variable. The normal component has an unknown variance, which we model as a function of the mean. We use B-splines to estimate the variance function during a training run on a suitable image, and the estimate is used to process subsequent images. Parameter estimates are generated for each image along with estimates of standard errors, and the number of dots in the image is determined using an information criterion and likelihood ratio tests. Realistic simulations show that our procedure is robust and that it leads to accurate estimates, both of parameters and of standard errors.

AB - We introduce a nearly automatic procedure to locate and count the quantum dots in images of kinesin motor assays. Our procedure employs an approximate likelihood estimator based on a two-component mixture model for the image data; the first component has a normal distribution, and the other component is distributed as a normal random variable plus an exponential random variable. The normal component has an unknown variance, which we model as a function of the mean. We use B-splines to estimate the variance function during a training run on a suitable image, and the estimate is used to process subsequent images. Parameter estimates are generated for each image along with estimates of standard errors, and the number of dots in the image is determined using an information criterion and likelihood ratio tests. Realistic simulations show that our procedure is robust and that it leads to accurate estimates, both of parameters and of standard errors.

KW - Bio-imaging

KW - Fluorescence microscopy

KW - Kinesin motor protein

KW - Maximum likelihood

KW - Mixture model

KW - Quantum dot

KW - Spline

KW - Variance-function estimation

UR - http://www.scopus.com/inward/record.url?scp=79959340985&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79959340985&partnerID=8YFLogxK

U2 - 10.1111/j.1541-0420.2010.01467.x

DO - 10.1111/j.1541-0420.2010.01467.x

M3 - Article

VL - 67

SP - 588

EP - 595

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 2

ER -