Propagating Uncertainty in Microarray Data Analysis

[edit]

Magnus Rattray, University of Manchester
Xuejun Liu, Nanjing University of Aeronautics and Astronautics
Guido Sanguinetti, University of Edinburgh
Marta Milo, University of Sheffield
Neil D. Lawrence, University of Sheffield

Briefings in Bioinformatics 7, pp 37-47

Related Material

Errata

  • The error bars in Figure 3 are incorrect, as explained in the errata to Liu et al. Bioinformatics 22, 2107-2113 \cite{Liu:variances06}.
    Thanks to: Richard Pearson

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.


@Article{rattray-propagating06,
  title = 	 {Propagating Uncertainty in Microarray Data Analysis},
  journal =  	 {Briefings in Bioinformatics},
  author = 	 {Magnus Rattray and Xuejun Liu and Guido Sanguinetti and Marta Milo and Neil D. Lawrence},
  pages = 	 {37},
  year = 	 {2006},
  volume = 	 {7},
  number =       {1},
  month = 	 {00},
  edit = 	 {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2006-01-01-rattray-propagating06.md},
  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.},
  key = 	 {Rattray-propagating06},
  linkpdf = 	 {http://bib.oxfordjournals.org/cgi/reprint/7/1/37},
  group = 	 {puma,shefml}
 

}
%T Propagating Uncertainty in Microarray Data Analysis
%A Magnus Rattray and Xuejun Liu and Guido Sanguinetti and Marta Milo and Neil D. Lawrence
%B 
%C Briefings in Bioinformatics
%D 
%F rattray-propagating06
%J Briefings in Bioinformatics	
%P 37--47
%R 
%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.
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
PY  - 2006/01/01
DA  - 2006/01/01	
ID  - rattray-propagating06	
SP  - 37
EP  - 47
L1  - http://bib.oxfordjournals.org/cgi/reprint/7/1/37
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  -

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