Bayesian Processing of Microarray Images

Neil D. LawrenceMarta MiloMahesan Niranjan, Penny Rashbass, Stephan Soullier
: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{pmlr-v-lawrence-microarray03, title = {Bayesian Processing of Microarray Images}, author = {Neil D. Lawrence and Marta Milo and Mahesan Niranjan and Penny Rashbass and Stephan Soullier}, pages = {71--80}, year = {}, editor = {}, 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 %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-microarray03 %I PMLR %J Proceedings of Machine Learning Research %P 71--80 %U http://inverseprobability.com %V %W PMLR %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 - PY - DA - ED - ID - pmlr-v-lawrence-microarray03 PB - PMLR SP - 71 DP - PMLR EP - 80 L1 - 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.. (). Bayesian Processing of Microarray Images. , in PMLR :71-80

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