Imperial College London

Dr Takoua Jendoubi

Faculty of Natural SciencesDepartment of Mathematics

Teaching Fellow in Statistics







533Huxley BuildingSouth Kensington Campus





Publication Type

3 results found

Jendoubi Bedhiafi T, Strimmer K, 2019, A whitening approach to probabilistic canonical correlation analysis for omics data integration, BMC Bioinformatics, Vol: 20, ISSN: 1471-2105

ackgroundCanonical correlation analysis (CCA) is a classic statistical tool for investigating complex multivariate data. Correspondingly, it has found many diverse applications, ranging from molecular biology and medicine to social science and finance. Intriguingly, despite the importance and pervasiveness of CCA, only recently a probabilistic understanding of CCA is developing, moving from an algorithmic to a model-based perspective and enabling its application to large-scale settings.ResultsHere, we revisit CCA from the perspective of statistical whitening of random variables and propose a simple yet flexible probabilistic model for CCA in the form of a two-layer latent variable generative model. The advantages of this variant of probabilistic CCA include non-ambiguity of the latent variables, provisions for negative canonical correlations, possibility of non-normal generative variables, as well as ease of interpretation on all levels of the model. In addition, we show that it lends itself to computationally efficient estimation in high-dimensional settings using regularized inference. We test our approach to CCA analysis in simulations and apply it to two omics data sets illustrating the integration of gene expression data, lipid concentrations and methylation levels.ConclusionsOur whitening approach to CCA provides a unifying perspective on CCA, linking together sphering procedures, multivariate regression and corresponding probabilistic generative models. Furthermore, we offer an efficient computer implementation in the “whitening” R package available at

Journal article

Jendoubi T, Ebbels TMD, Integrative analysis of time course metabolic data and biomarker discovery, AMLICD workshop NIPS 2017

Metabonomics time-course experiments provide the opportunity to understandthe changes to an organism by observing the evolution of metabolic profiles inresponse to internal or external stimuli. Along with other omic longitudinalprofiling technologies, these techniques have great potential to complement theanalysis of complex relations between variations across diverse omic variablesand provide unique insights into the underlying biology of the system. However,many statistical methods currently used to analyse short time-series omic dataare i) prone to overfitting or ii) do not take into account the experimentaldesign or iii) do not make full use of the multivariate information intrinsicto the data or iv) unable to uncover multiple associations between differentomic data. The model we propose is an attempt to i) overcome overfitting byusing a weakly informative Bayesian model, ii) capture experimental designconditions through a mixed-effects model, iii) model interdependencies betweenvariables by augmenting the mixed-effects model with a conditionalauto-regressive (CAR) component and iv) identify potential associations betweenheterogeneous omic variables .

Conference paper

Jendoubi T, Bergeaud V, Geay A, 2014, Comparison of some parallelisation strategies of thermalhydraulics codes on GPUs, Joint 8th International Conference on Supercomputing in Nuclear Applications (SNA) / 4th Monte Carlo Meeting (MC), Publisher: E D P SCIENCES

Conference paper

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