Biomedical subjects

Timothy M D Ebbels

Publications and source records attributed to Timothy M D Ebbels.

4 recordsLinked to original sources

PubMed2006

springScape: visualisation of microarray and contextual bioinformatic data using spring embedding and an 'information landscape'.

The interpretation of microarray and other high-throughput data is highly dependent on the biological context of experiments. However, standard analysis packages are poor at simultaneously presenting both the array and related bioinformatic data. We have addressed this challenge by developing a system springScape based on 'spring embedding' and an 'information landscape' allowing several related data sources to be dynamically combined while highlighting one particular feature. Each data source is represented as a network of nodes connected by weighted edges. The networks are combined and embedded in the 2-D plane by spring embedding such that nodes with a high similarity are drawn close together. Complex relationships can be discovered by varying the weight of each data source and observing the dynamic response of the spring network. By modifying Procrustes analysis, we find that the visualizations have an acceptable degree of reproducibility. The 'information landscape' highlights one particular data source, displaying it as a smooth surface whose height is proportional to both the information being viewed and the density of nodes. The algorithm is demonstrated using several microarray data sets in combination with protein-protein interaction data and GO annotations. Among the features revealed are the spatio-temporal profile of gene expression and the identification of GO terms correlated with gene expression and protein interactions. The power of this combined display lies in its interactive feedback and exploitation of human visual pattern recognition. Overall, springScape shows promise as a tool for the interpretation of microarray data in the context of relevant bioinformatic information.

Algorithms↗

PubMed2004

Evaluation of metabolic variation in normal rat strains from a statistical analysis of 1H NMR spectra of urine.

NMR spectroscopic and statistical methods have been applied to investigate the biochemical variations within and between two phenotypically normal rat strains. The 600 MHz (1)H NMR spectra of urine were acquired as part of a series of drug toxicity studies from 450 control rat urine samples from each of two strains of laboratory rat (Han Wistar and Sprague Dawley). The spectra were data-reduced to 256 intensity descriptors over a range of delta 0.2-10.0. The spectral variation was analysed both within and between strains in terms of the mean, standard deviation, skewness and kurtosis of each descriptor. It is demonstrated that spectral intensities corresponding to a number of endogenous metabolites do not show Gaussian distributions and there is evidence for bimodality for some metabolites. Additionally, despite the visual similarity of the NMR spectra from the two strains of rat, the descriptor distributions and the statistics derived from them revealed differences in the metabolite profiles, which clearly distinguished the two populations. This work is of value in the determination of biochemical normality and variability, and thus can be used to investigate, and place confidence limits on the biochemical deviations, which arise as a consequence of genetic modification or pathophysiological events.

Animals↗

PubMed2004

Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles.

Metabonomics can be viewed as the process of defining multivariate metabolic trajectories that describe the systemic response of organisms to physiological perturbations through time. We have explored the hypothesis that the homothetic geometry of a metabolic trajectory, i.e., the metabolic response irrespective of baseline values and overall magnitude, defines the mode of response of the organism to treatment and is hence the key property when considering the similarity between two sets of measurements. A modeling strategy to test for homothetic geometry, called scaled-to-maximum, aligned, and reduced trajectories (SMART) analysis, is presented that together with principal components analysis (PCA) facilitates the visualization of multivariate response similarity and hence the interpretation of metabonomic data. Several examples of the utility of this approach from toxicological studies are presented as follows: interlaboratory variation in hydrazine response, CCl(4) dose-response relationships, and interspecies comparison of bromobenzene toxicity. In each case, the homothetic trajectories hypothesis is shown to be an important concept for the successful multivariate modeling and interpretation of systemic metabolic change. Overall, geometric trajectory analysis based on a homothetic modeling strategy like SMART facilitates the amalgamation and comparison of metabonomic data sets and can improve the accuracy and precision of classification models based on metabolic profile data. Because interlaboratory variation, normal physiological variation, dose-response relationships, and interspecies differences are also key areas of concern in genomic and proteomic as well as metabonomic studies, the methods presented here may also have an impact on many other multilaboratory efforts to produce screenable "-omics" databases useful for gauging toxicity in safety assessment and drug discovery.

Animals↗

PubMed2002

Analytical reproducibility in (1)H NMR-based metabonomic urinalysis.

Metabonomic analysis of biofluids and tissues utilizing high-resolution NMR spectroscopy and chemometric techniques has proven valuable in characterizing the biochemical response to toxicity for many xenobiotics. To assess the analytical reproducibility of metabonomic protocols, sample preparation and NMR data acquisition were performed at two sites (one using a 500 MHz and the other using a 600 MHz system) using two identical (split) sets of urine samples from an 8-day acute study of hydrazine toxicity in the rat. Despite the difference in spectrometer operating frequency, both datasets were extremely similar when analyzed using principal components analysis (PCA) and gave near-identical descriptions of the metabolic responses to hydrazine treatment. The main consistent difference between the datasets was related to the efficiency of water resonance suppression in the spectra. In a 4-PC model of both datasets combined, describing all systematic dose- and time-related variation (88% of the total variation), differences between the two datasets accounted for only 3% of the total modeled variance compared to ca. 15% for normal physiological (pre-dose) variation. Furthermore, <3% of spectra displayed distinct inter-site differences, and these were clearly identified as outliers in their respective dose-group PCA models. No samples produced clear outliers in both datasets, suggesting that the outliers observed did not reflect an unusual sample composition, but rather sporadic differences in sample preparation leading to, for example, very dilute samples. Estimations of the relative concentrations of citrate, hippurate, and taurine were in >95% correlation (r(2)) between sites, with an analytical error comparable to normal physiological variation in concentration (4-8%). The excellent analytical reproducibility and robustness of metabonomic techniques demonstrated here are highly competitive compared to the best proteomic analyses and are in significant contrast to genomic microarray platforms, both of which are complementary techniques for predictive and mechanistic toxicology. These results have implications for the quantitative interpretation of metabonomic data, and the establishment of quality control criteria for both regulatory agencies and for integrating data obtained at different sites.

Animals↗