Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference

Neil D. LawrenceMarta MiloMahesan Niranjan, Penny Rashbass, Stephan Soullier
,  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 .

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-variability03, title = {Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference}, author = {Neil D. Lawrence and Marta Milo and Mahesan Niranjan and Penny Rashbass and Stephan Soullier}, pages = {518--526}, year = {}, editor = {}, volume = {20}, number = {4}, url = {http://inverseprobability.com/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 Conference Paper %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 %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-variability03 %I PMLR %J Proceedings of Machine Learning Research %P 518--526 %R 10.1093/bioinformatics/btg438 %U http://inverseprobability.com %V %N 4 %W PMLR %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 - CPAPER 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 BT - PY - DA - ED - ID - pmlr-v-lawrence-variability03 PB - PMLR SP - 518 DP - PMLR EP - 526 DO - 10.1093/bioinformatics/btg438 L1 - UR - http://inverseprobability.com/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.. (). Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference. , in PMLR (4):518-526

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