Kernels, regularization and differential equations
Pattern Recognition
Cross-Platform Analysis with Binarized Gene Expression Data
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Matrix factorisation methods applied in microarray data analysis
International Journal of Data Mining and Bioinformatics
A Weighted Principal Component Analysis and Its Application to Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: 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. Results: 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 dataset 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. Availability: The software used in the paper is available from http://www.bioinf.man.ac.uk/resources/puma. The microarray data are depo-sited in the NCBI database. Contact: neil@dcs.shef.ac.uk