


... 


Panagiotis Papastamoulis 

Address:
Faculty of Biology, Medicine and Health 

Division of Informatics, Imaging and Data Sciences 

University of Manchester 

Oxford Road, Manchester M13 9PL, UK. 

Office:
B.1082 Michael Smith building. 

Education
2003: 

Dipl. in
Mathematics, Department of Mathematics, University
of Patras, Greece 
2005:


MSc in Applied
Statistics, Department of Statistics and Insurance Science, University
of Piraeus, Greece 
2010:


Ph.D. in
Statistics, Department of Statistics and Insurance Science, University
of Piraeus, Greece 
Academic Positions
2011  2012: 

Research Associate at URGV  Plant Genomics Research, INRA, Evry, France. 
2012  : 

Research Associate at the University of Manchester, FBMH: informatics, imaging and data sciences. 
Research Interests
My research combines statistical inference on latent class models with computationally intensive
applications. I have worked extensively on mixture models mainly from a Bayesian perspective.
My PhD thesis proposed a solution to the label switching problem in Bayesian analysis of mixtures as
well as a modification of the reversible jump MCMC algorithm for univariate normal mixtures.
As a post doc researcher at URGV plant genomic unit I developed an initialization scheme of the EM algorithm
for the efficient estimation of Poisson GLM mixtures. The method has been applied to real high throughput
sequencing data with large number of components.
Currently I'm working at the University of Manchester with Professor Magnus Rattray. Our project includes tasks
such as the development of Bayesian methods for estimating transcript expression and performing differential
expression analysis in next generation sequencing data, clustering biomedical data as well as inferring changepoints
in replicated timeseries applied to growth data modelling.
Publications in refereed international journals
1. Papastamoulis P. and Iliopoulos G. (2009). Reversible Jump MCMC in mixtures of normal distributions with the same component means. Computational Statistics and Data Analysis, 53: 900911.
2. Papastamoulis P. and Iliopoulos G. (2010). An artificial allocations based solution to the label switching problem in Bayesian analysis of mixtures of distributions. Journal of Computational and Graphical Statistics, 19: 313331.
3. Papastamoulis P. and Iliopoulos G. (2013). On the convergence rate of Random Permutation Sampler and ECR algorithm in missing data models.
Methodology and Computing in Applied Probability, 15(2): 293304.
4. Papastamoulis P. (2014). Handling the label switching problem in latent class models via the ECR algorithm.
Communications in Statistics, Simulation and Computation, 43(4): 913927.
5. Papastamoulis P., Hensman, J., Glaus, P. and Rattray, M. (2014). Improved variational Bayes inference for transcript expression estimation. Statistical Applications in Genetics and Molecular Biology, 13(2): 203216.
6. *Hensman, J., *Papastamoulis P., Glaus P., Honkela A. and Rattray M. (2015). Fast and accurate approximate inference of transcript expression from RNAseq data. Bioinformatics, 31(24): 38813889
7. Papastamoulis P., MartinMagniette, M.L. and MaugisRabusseau, C. (2016). On the estimation of mixtures of Poisson regression models with large number of components. Computational Statistics and Data Analysis, 93 (3rd Special Issue on Advances in Mixture Models): 97106.
8. Papastamoulis P. (2016). label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs. Journal of Statistical Software, 69(1): 124.
9. Papastamoulis P. and Rattray M. (2017). A Bayesian model selection approach for identifying differentially expressed transcripts from RNASeq data. Journal of the Royal Statistical Society, Series C, doi: 10.1111/rssc.12213. preprint: arXiv:1412.3050 [stat.ME]
10. Papastamoulis P. and Rattray M. (2017). BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data. The R Journal, 9(1): 403420. R script for reproducing the results.
11. Papastamoulis P. and Rattray M. (2017). Bayesian estimation of Differential Transcript Usage from RNAseq data. Statistical Applications in Genetics and Molecular Biology (accepted). ahead of print link
12. Howard R., Belgrave D., Papastamoulis P., Simpson A., Rattray M. and Custovic, A. (2017). Evolution of IgE responses to multiple allergen components throughout childhood. Journal of Allergy and Clinical Immunology (accepted).
Preprints
13. Papastamoulis P. (2017). Overfitting Bayesian Mixtures of Factor Analyzers with an Unknown Number of Components. arXiv:1701.04605 [stat.ME] [submitted + revised]
14. Papastamoulis P., Furukawa T., van Rhijn N., Bromley M., Bignell E. and Rattray M. (2017). Bayesian detection of piecewise linear trends in replicated timeseries with application to growth data modelling. arXiv:1709.06111 [stat.AP] [submitted]
Software
1. Papastamoulis P. and Iliopoulos G. (2010). ecr_urb.Rnw: Rweave code for applying the ECR algorithm to simulated MCMC output of univariate normal mixture models. Suplementary material of the article: An artificial allocations based solution to the label switching problem in Bayesian analysis of mixtures of distributions. Journal of Computational and Graphical Statistics, 19: 313331.
2. Papastamoulis P., MartinMagniette M.L and MaugisRabusseau C. (2012). poisson.glm.mix: R package for the estimation of high dimensional mixtures of Poisson GLMs.
3. Papastamoulis P., Hensman, J., Glaus, P. and Rattray, M. (2013). gen_dir_vb: C++ source code for approximating the posterior distribution of mixture weights using Variational Bayes.
4. Papastamoulis P. (2013). label.switching: R package for dealing with label switching problem in MCMC outputs of mixture models.
5. Papastamoulis P. and Rattray, M. (2015). cjBitSeq: Differential Expression Analysis algorithm
6. Papastamoulis P. (2016). BayesBinMix: Bayesian Estimation of Mixtures of Multivariate Bernoulli Distributions.
7. Papastamoulis P. (2017). fabMix: Overfitting Bayesian Mixtures of Factor Analyzers.
8. Papastamoulis P. (2017). growthPhaseMCMC: Bayesian changepoint detection in growth timeseries.