Propagating Uncertainty in Microarray Data Analysis

Magnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence
Briefings in Bioinformatics, 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
@Article{Rattray-propagating06, title = {Propagating Uncertainty in Microarray Data Analysis}, author = {Rattray, Magnus and Liu, Xuejun and Sanguinetti, Guido and Milo, Marta and Lawrence, Neil D.}, journal = {Briefings in Bioinformatics}, pages = {37--47}, year = {2006}, 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 Journal Article %T Propagating Uncertainty in Microarray Data Analysis %A Magnus Rattray %A Xuejun Liu %A Guido Sanguinetti %A Marta Milo %A Neil D. Lawrence %J Briefings in Bioinformatics %D 2006 %F Rattray-propagating06 %P 37--47 %U http://inverseprobability.com/publications/rattray-propagating06.html %V 7 %N 1 %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 - JOUR TI - Propagating Uncertainty in Microarray Data Analysis AU - Magnus Rattray AU - Xuejun Liu AU - Guido Sanguinetti AU - Marta Milo AU - Neil D. Lawrence DA - 2006/01/01 ID - Rattray-propagating06 VL - 7 IS - 1 SP - 37 EP - 47 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.. (2006). Propagating Uncertainty in Microarray Data Analysis. Briefings in Bioinformatics 7(1):37-47 Available from http://inverseprobability.com/publications/rattray-propagating06.html.

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