Noise and nonlinearities in high-throughput data

High-throughput data analyses are becoming common in biology, communications, economics and sociology. The vast amounts of data are usually represented in the form of matrices and can be considered as knowledge networks. Spectra-based approaches have proved useful in extracting hidden information within such networks and for estimating missing data, but these methods are based essentially on linear assumptions. The physical models of matching, when applicable, often suggest non-linear mechanisms, that may sometimes be identified as noise. The use of non-linear models in data analysis, however, may require the introduction of many parameters, which lowers the statistical weight of the model. According to the quality of data, a simpler linear analysis may be more convenient than more complex approaches. In this paper, we show how a simple non-parametric Bayesian model may be used to explore the role of non-linearities and noise in synthetic and experimental data sets.Comment: 12 pages, 3 figure

Improved prediction of protein interaction from microarray data using asymmetric correlation

Background:Detection of correlated gene expression is a fundamental process in the characterization of gene functions using microarray data. Commonly used methods such as the Pearson correlation can detect only a fraction of interactions between genes or their products. However, the performance of correlation analysis can be significantly improved either by providing additional biological information or by combining correlation with other techniques that can extract various mathematical or statistical properties of gene expression from microarray data. In this article, I will test the performance of three correlation methods-the Pearson correlation, the rank (Spearman) correlation, and the Mutual Information approach-in detection of protein-protein interactions, and I will further examine the properties of these techniques when they are used together. I will also develop a new correlation measure which can be used with other measures to improve predictive power.
 
Results:Using data from 5,896 microarray hybridizations, the three measures were obtained for 30,499 known protein-interacting pairs in the Human Protein Reference Database (HPRD). Pearson correlation showed the best sensitivity (0.305) but the three measures showed similar specificity (0.240 - 0.257). When the three measures were compared, it was found that better specificity could be obtained at a high Pearson coefficient combined with a low Spearman coefficient or Mutual Information. Using a toy model of two gene interactions, I found that such measure combinations were most likely to exist at stronger curvature. I therefore introduced a new measure, termed asymmetric correlation (AC), which directly quantifies the degree of curvature in the expression levels of two genes as a degree of asymmetry. I found that AC performed better than the other measures, particularly when high specificity was required. Moreover, a combination of AC with other measures significantly improved specificity and sensitivity, by up to 50%. 
 
Conclusions: A combination of correlation measures, particularly AC and Pearson correlation, can improve prediction of protein-protein interactions. Further studies are required to assess the biological significance of asymmetry in expression patterns of gene pairs. 
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Spectral analysis of gene expression profiles using gene networks

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. Here we propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We applied the method to the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. It performed at least as well as the usual classification but provides much more biologically relevant results and allows a direct biological interpretation

Bisociative knowledge discovery for microarray data analysis

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