Bayesian Processing of Microarray Images

Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier
Neural Networks for Signal Processing XIII, IEEE :71-80, 2003.

Abstract

Gene expression measurements quantify the level of mRNA produced from each gene. Two principal methods exist for producing slides for extracting these levels: photolithography and spotted arrays. One difficulty with the spotted array format is determining the size and location of the spots on the array. In this paper we present a Bayesian approach to processing images produced by these arrays that seeks posterior distributions over the size and positions of the spots. This enables us to estimate expression ratios and their variances. Exact inference for the model we specify is intractable; we develop an approximate inference technique which combines importance sampling with variational inference. Our technique has already been shown to be more consistent than both manual processing and another automated technique @Lawrence:variability03. Here we present large-scale results for twenty-four microarray slides each representing 5760 genes and show the dramatic effects of incorporating variance in our downsteam analysis. Software based on this algorithm is available for academic use.

Cite this Paper

BibTeX
@InProceedings{Lawrence:microarray03, title = {Bayesian Processing of Microarray Images}, author = {Lawrence, Neil D. and Milo, Marta and Niranjan, Mahesan and Rashbass, Penny and Soullier, Stephan}, booktitle = {Neural Networks for Signal Processing XIII}, pages = {71--80}, year = {2003}, editor = {Molina, Christophe and Adali, Tülay and Larsen, Jan and Hulle, Marc Van and Douglas, Scott and Rouat, Jean}, publisher = {IEEE}, doi = {10.1109/NNSP.2003.1318005}, pdf = {https://inverseprobability.com/publications/files/visMicroarray.pdf}, url = {http://inverseprobability.com/publications/lawrence-microarray03.html}, abstract = {Gene expression measurements quantify the level of mRNA produced from each gene. Two principal methods exist for producing slides for extracting these levels: photolithography and spotted arrays. One difficulty with the spotted array format is determining the size and location of the spots on the array. In this paper we present a Bayesian approach to processing images produced by these arrays that seeks posterior distributions over the size and positions of the spots. This enables us to estimate expression ratios and their variances. Exact inference for the model we specify is intractable; we develop an approximate inference technique which combines importance sampling with variational inference. Our technique has already been shown to be more consistent than both manual processing and another automated technique @Lawrence:variability03. Here we present large-scale results for twenty-four microarray slides each representing 5760 genes and show the dramatic effects of incorporating variance in our downsteam analysis. Software based on this algorithm is available for academic use.} }
Endnote
%0 Conference Paper %T Bayesian Processing of Microarray Images %A Neil D. Lawrence %A Marta Milo %A Mahesan Niranjan %A Penny Rashbass %A Stephan Soullier %B Neural Networks for Signal Processing XIII %D 2003 %E Christophe Molina %E Tülay Adali %E Jan Larsen %E Marc Van Hulle %E Scott Douglas %E Jean Rouat %F Lawrence:microarray03 %I IEEE %P 71--80 %R 10.1109/NNSP.2003.1318005 %U http://inverseprobability.com/publications/lawrence-microarray03.html %X Gene expression measurements quantify the level of mRNA produced from each gene. Two principal methods exist for producing slides for extracting these levels: photolithography and spotted arrays. One difficulty with the spotted array format is determining the size and location of the spots on the array. In this paper we present a Bayesian approach to processing images produced by these arrays that seeks posterior distributions over the size and positions of the spots. This enables us to estimate expression ratios and their variances. Exact inference for the model we specify is intractable; we develop an approximate inference technique which combines importance sampling with variational inference. Our technique has already been shown to be more consistent than both manual processing and another automated technique @Lawrence:variability03. Here we present large-scale results for twenty-four microarray slides each representing 5760 genes and show the dramatic effects of incorporating variance in our downsteam analysis. Software based on this algorithm is available for academic use.
RIS
TY - CPAPER TI - Bayesian Processing of Microarray Images AU - Neil D. Lawrence AU - Marta Milo AU - Mahesan Niranjan AU - Penny Rashbass AU - Stephan Soullier BT - Neural Networks for Signal Processing XIII DA - 2003/09/17 ED - Christophe Molina ED - Tülay Adali ED - Jan Larsen ED - Marc Van Hulle ED - Scott Douglas ED - Jean Rouat ID - Lawrence:microarray03 PB - IEEE SP - 71 EP - 80 DO - 10.1109/NNSP.2003.1318005 L1 - https://inverseprobability.com/publications/files/visMicroarray.pdf UR - http://inverseprobability.com/publications/lawrence-microarray03.html AB - Gene expression measurements quantify the level of mRNA produced from each gene. Two principal methods exist for producing slides for extracting these levels: photolithography and spotted arrays. One difficulty with the spotted array format is determining the size and location of the spots on the array. In this paper we present a Bayesian approach to processing images produced by these arrays that seeks posterior distributions over the size and positions of the spots. This enables us to estimate expression ratios and their variances. Exact inference for the model we specify is intractable; we develop an approximate inference technique which combines importance sampling with variational inference. Our technique has already been shown to be more consistent than both manual processing and another automated technique @Lawrence:variability03. Here we present large-scale results for twenty-four microarray slides each representing 5760 genes and show the dramatic effects of incorporating variance in our downsteam analysis. Software based on this algorithm is available for academic use. ER -
APA
Lawrence, N.D., Milo, M., Niranjan, M., Rashbass, P. & Soullier, S.. (2003). Bayesian Processing of Microarray Images. Neural Networks for Signal Processing XIII:71-80 doi:10.1109/NNSP.2003.1318005 Available from http://inverseprobability.com/publications/lawrence-microarray03.html.

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