PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Accounting for probe-level noise in principal component analysis of microarray data
Guido Sanguinetti, Marta Milo, magnus rattray and Neil Lawrence
Bioinformatics Volume 21, Number 19, pp. 3748-3754, 2005.


Principal Component Analysis (PCA) is one of the most popular dimensionality reduction techniques for the analysis of high dimensional datasets. However, in its standard form, it does not take into account any error measures associated with the data points beyond a standard spherical noise. This indiscriminate nature provides one of its main weaknesses when applied to biological data with inherently large variability, such as expression levels measured with microarrays. Methods now exist for extracting credibility intervals from the probe-level analysis of cDNA and oligonucleotide microarray experiments. These credibility intervals are gene and experiment specific, and can be propagated through an appropriate probabilistic downstream analysis. We propose a new model-based approach to PCA that takes into account the variances associated with each gene in each experiment. We develop an efficient EM-algorithm to estimate the parameters of our new model. The model provides significantly better results than standard PCA, while remaining computationally reasonable. We show how the model can be used to `denoise' a microarray data set leading to improved expression profiles and tighter clustering across profiles. The probabilistic nature of the model means that the correct number of principal components is automatically obtained.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1555
Deposited By:Guido Sanguinetti
Deposited On:28 November 2005