PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Propagating Uncertainty in Microarray Data Analysis
Magnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo and Neil Lawrence
Briefings in Bioinformatics 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2288
Deposited By:Guido Sanguinetti
Deposited On:28 October 2006