I am a senior lecturer within the Biomolecular Medicine Section of the Department of Surgery and Cancer. My overall research interests lie at the interface between two broad areas:

• multivariate data analysis, and
• post-genomic technologies.

More specifically, on the computational side these include, machine learning, bioinformatics, chemometrics, and multivariate statistics, and on the experimental side, the fields of genomics, proteomics and metabonomics / metabolomics. I am interested in applying diverse computational and mathematical methods in order to disentangle the mass of information at multiple biological levels generated by the –omics technologies. The ultimate aim is to synthesise the different information provided by each of these techniques, thus facilitating the modular approach to understanding biological function known as systems biology. This broad aim leads to two specific themes in my research:

• Development of novel chemometrics and machine learning algorithms.
• Application of chemometrics and machine learning to post-genomic data.

Projects

Simulation of NMR metabolic profiles

With Dr Maria De Iorio, now at UCL, we have developed a MATLAB program which is able to simulate realistic 1-dimensional ¹H NMR spectra of complex mixtures of metabolites. The programme 'MetAssimulo' allows the user to specify the levels and variability of metabolites to be incorporated, and by default will simulate two groups corresponding to 'cases' and 'controls'. MetAssimulo can also simulate correlations within and between metabolites, as well as shifts in peak positions often seen in metabolic spectra. The picture below shows two NMR spectra of human urine. One is real and one is simulated. Can you tell which is which?

For more information please visit the MetAssimulo page or read the paper.

Rigorous statistical models of liquid chromatography mass spectrometry (LC-MS) data

LC-MS is one of the most widely used technologies in metabolomics and other bioanalytical sciences today. The measurement process is very complex and thus most methods for processing the data rely on heuristic approaches. With Andreas Ipsen we have developed a new model of LC-Time of Flight-MS, based on the physical ion counting process at the detector, which is able to model the data more accurately than ever before. The model has so far led to two applications: detection of co-eluting compounds and construction of rigorous confidence intervals on isotope ratios. Both of these applications help in the difficult task of identifying unknown molecules - one of the key bottlenecks in metabolomics today. The picture below shows the ion counts from two partially coeluting species. The colours display the p-value from the new test indicating clearly that these two peaks do not coelute. More details in the papers 1 & 2.

Bayesian analysis of metabolic NMR spectra

In this collaboration with Dr Maria De Iorio, UCL, we have developed a Bayesian model of NMR spectra which aims to automatically assign and quantify resonances of metabolites in complex 1-dimensional spectra of biofluids and tissues. We are releasing a publically available R package called the Bayesian AuTomated Metabolite Analyser for NMR spectra (BATMAN).

Download the software:

[Note: .tgz and .tar.gz files may download without the correct extension. You may have to add the correct extension before installing.]

Batman 1.0.3 (latest version)

• Source code (linux/unix) batman_1.0.3.tar.gz
• Mac binary batman_1.0.3.tgz (requires R 2.15.0 or later)
• Win binary batman_1.0.3.zip
• Installation
• Documentation

Batman 1.0.2

• source code (linux/unix systems)
• mac binary (compatible with R 2.12.1 to 2.14)
• win binary

Time series analysis in metabolomics

With Giovanni Montana and Maurice Berk we have developed new techniques for time series analysis in metabolomics. Traditional time series methods require hundreds of time points and typically monitor just a few variables. In metabolic profiling, we monitor hundreds to thousands of variables over just a few (typicall 3-10) time points. The plot below shows an example from our smoothing splines mixed effects (SME) model where the time course for two different groups of animals is being compared for a single metabolite. See publication.

Pathway tools for integrating multi-omics data

With Hector Keun, Rachel Cavill (Imperial) and Ralf Herwig and Atans Kamburov (MPIMG, Germany) we have developed new tools which allow researchers to interpret data from multiple omics experiments in a pathway context. The pathway approach allows a higher sensitivity and more global overview of the effects in a biological study and is particularly suited to integrating data from different levels of biomolecular organisation (e.g. metabolomics, transcriptomics) obtained in heterogeneous experiments. The figure blow shows a clustering of 118 drugs (rows) with hundreds of pathways (columns) indicating the overall pathway response of different cancer cell lines to drug treatment, as measured by metabolomics and transcriptomics. See the paper.

 Differential Correlation Networks

A characteristic of omics data is that the individual measurements are highly correlated. That is, the level of a gene or metabolite tends to vary in a similar way to that of other genes/metabolites. These correlations can be visualised as a network in which correlated molecules are connected. The correlation network can be thought of as a new type of 'fingerprint' of the biological status of an organism and is therefore of interest in its own right. With Maria De Iorio (UCL) we have developed methods for analysing how correlation networks change in response to different biological conditions. We term these 'differential correlation networks'. The picture below shows which links in the blood lipoprotein correlation network are different bewteen normal people and those with pre-diabetics symptoms. See the paper.

Classification tools for metabolomics

COMET Expert System: This figure shows a schematic of an 'expert system' constructed to help predict the toxicity of novel drugs. On the left NMR spectra of rat urine are shown (a,b), a model of normality is developed (c) over a time course (d). The right panel shows a similarity matrix comparing metabolic profiles from 62 treatments affecting the liver or kidney reflecting a high degree of organ specificity. See Ebbels et al.  J. Prot. Res. 2007.