Reducing the Variability in <span>cDNA</span> Microarray Image Processing by <span>B</span>ayesian Inference

Neil D. Lawrence; Marta Milo; Mahesan Niranjan; Penny Rashbass; Stephan Soullier

doi:10.1093/bioinformatics/btg438

edit

Back to publications

Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference

Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier

Bioinformatics, 20(4):518-526, 2004.

Abstract

Motivation: Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray ‘grids’. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray ‘spots’, capturing uncertainty that can be passed to downstream analysis.

Results: Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers’ time normally spent in refining the grid placement.

Availability: A MATLAB implementation of the algorithm and an image of the slide used in our experiments, as well as the code necessary to recreate them are available for non-commercial use from http://inverseprobability.com/vis.

Links

Cite this Paper

BibTeX


@Article{Lawrence-variability03,
  title = 	 {Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference},
  author = 	 {Lawrence, Neil D. and Milo, Marta and Niranjan, Mahesan and Rashbass, Penny and Soullier, Stephan},
  journal =      {Bioinformatics},
  pages = 	 {518--526},
  year = 	 {2004},
  volume = 	 {20},
  number =       {4},
  doi = 	 {10.1093/bioinformatics/btg438},
  url = 	 {/publications/lawrence-variability03.html},
  abstract = 	 {**Motivation:** Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray ‘grids’. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray ‘spots’, capturing uncertainty that can be passed to downstream analysis.\
\
**Results:** Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers’ time normally spent in refining the grid placement.\
\
**Availability:** A MATLAB implementation of the algorithm and an image of the slide used in our experiments, as well as the code necessary to recreate them are available for non-commercial use from .}
}

Endnote

%0 Journal Article
%T Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference
%A Neil D. Lawrence
%A Marta Milo
%A Mahesan Niranjan
%A Penny Rashbass
%A Stephan Soullier
%J Bioinformatics
%D 2004	
%F Lawrence-variability03
%P 518--526
%R 10.1093/bioinformatics/btg438
%U /publications/lawrence-variability03.html
%V 20
%N 4
%X **Motivation:** Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray ‘grids’. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray ‘spots’, capturing uncertainty that can be passed to downstream analysis.\
\
**Results:** Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers’ time normally spent in refining the grid placement.\
\
**Availability:** A MATLAB implementation of the algorithm and an image of the slide used in our experiments, as well as the code necessary to recreate them are available for non-commercial use from .

RIS


TY  - JOUR
TI  - Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference
AU  - Neil D. Lawrence
AU  - Marta Milo
AU  - Mahesan Niranjan
AU  - Penny Rashbass
AU  - Stephan Soullier
DA  - 2004/01/22	
ID  - Lawrence-variability03
VL  - 20
IS  - 4
SP  - 518
EP  - 526
DO  - 10.1093/bioinformatics/btg438
UR  - /publications/lawrence-variability03.html
AB  - **Motivation:** Gene expression levels are obtained from microarray experiments through the extraction of pixel intensities from a scanned image of the slide. It is widely acknowledged that variabilities can occur in expression levels extracted from the same images by different users with the same software packages. These inconsistencies arise due to differences in the refinement of the placement of the microarray ‘grids’. We introduce a novel automated approach to the refinement of grid placements that is based upon the use of Bayesian inference for determining the size, shape and positioning of the microarray ‘spots’, capturing uncertainty that can be passed to downstream analysis.\
\
**Results:** Our experiments demonstrate that variability between users can be significantly reduced using the approach. The automated nature of the approach also saves hours of researchers’ time normally spent in refining the grid placement.\
\
**Availability:** A MATLAB implementation of the algorithm and an image of the slide used in our experiments, as well as the code necessary to recreate them are available for non-commercial use from .
ER  -

APA


Lawrence, N.D., Milo, M., Niranjan, M., Rashbass, P. & Soullier, S.. (2004). Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference. Bioinformatics 20(4):518-526 doi:10.1093/bioinformatics/btg438 Available from /publications/lawrence-variability03.html.