Propagating Uncertainty in Microarray Data Analysis

Magnus RattrayXuejun LiuGuido SanguinettiMarta MiloNeil D. Lawrence
,  7(1):37-47, 2006.

Abstract

Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip® array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-rattray-propagating06, title = {Propagating Uncertainty in Microarray Data Analysis}, author = {Magnus Rattray and Xuejun Liu and Guido Sanguinetti and Marta Milo and Neil D. Lawrence}, pages = {37--47}, year = {}, editor = {}, volume = {7}, number = {1}, url = {http://inverseprobability.com/publications/rattray-propagating06.html}, abstract = {Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip® array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.} }
Endnote
%0 Conference Paper %T Propagating Uncertainty in Microarray Data Analysis %A Magnus Rattray %A Xuejun Liu %A Guido Sanguinetti %A Marta Milo %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-rattray-propagating06 %I PMLR %J Proceedings of Machine Learning Research %P 37--47 %U http://inverseprobability.com %V %N 1 %W PMLR %X Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip® array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis.
RIS
TY - CPAPER TI - Propagating Uncertainty in Microarray Data Analysis AU - Magnus Rattray AU - Xuejun Liu AU - Guido Sanguinetti AU - Marta Milo AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-rattray-propagating06 PB - PMLR SP - 37 DP - PMLR EP - 47 L1 - UR - http://inverseprobability.com/publications/rattray-propagating06.html AB - Microarray technology is associated with many sources of experimental uncertainty. In this review we discuss a number of approaches for dealing with this uncertainty in the processing of data from microarray experiments. We focus here on the analysis of high-density oligonucleotide arrays, such as the popular Affymetrix GeneChip® array, which contain multiple probes for each target. This set of probes can be used to determine an estimate for the target concentration and can also be used to determine the experimental uncertainty associated with this measurement. This measurement uncertainty can then be propagated through the downstream analysis using probabilistic methods. We give examples showing how these credibility intervals can be used to help identify differential expression, to combine information from replicated experiments and to improve the performance of principal component analysis. ER -
APA
Rattray, M., Liu, X., Sanguinetti, G., Milo, M. & Lawrence, N.D.. (). Propagating Uncertainty in Microarray Data Analysis. , in PMLR (1):37-47

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