Dr Thomas Bartlett

* @ucl.ac.uk

Research Interests

Stochastic networks; stochastic processes/time-series analysis; computational statistics; models of genomic and epigenomic data.

Awards/Prizes

• 2017 MRC Fellowship (skills development, quantitative). Awarded by U.K. research council, to fund three years’ independent work developing novel statistical methodology for biomedical science applications.
• 2015 EPSRC Doctoral prize. Fellowship awarded by U.K. research council, to fund two years’ independent postdoctoral work following my PhD.
• 2013 Bogue Fellowship awarded by University College London, to fund a 4-month research visit to Columbia University, New York, U.S.A.

Manuscripts in Preparation

• Two-way sparsity for time-varying networks, with applications in genomics. Bartlett TE, Kosmidis, I, Silva, R. Preprint available: http://arxiv.org/abs/1802.08114
• Co-modularity and co-community detection in large networks. Bartlett TE, Olhede SC. Preprint available: http://arxiv.org/abs/1511.05611

Published Work

• 2017 Single-cell Co-expression Subnetwork Analysis. Bartlett TE, Müller S, Diaz A. Nature Scientific Reports 7(1): 15066
• 2017 Network inference and community detection, based on covariance matrices, correlations and test statistics from arbitrary distributions. Bartlett TE. Communications in Statistics - Theory and Methods 46(18): 9150-9165
• 2017 Parenclitic network analysis of methylation data for cancer identification. Karsakov A, Bartlett TE, Ryblov A, Meyerov I, Ivanchenko M, Zaikin A. PloS one, 12(1): e0169661
• 2016 Detection of Epigenomic Network Community Oncomarkers. Bartlett TE, Zaikin A. Annals of Applied Statistics 10(3): 1373-1396. Preprint available: http://arxiv.org/abs/1506.05244
• 2016 Epigenetic reprogramming of Fallopian tube fimbriae in BRCA mutation carriers defines early ovarian cancer evolution. Bartlett TE, Widschwendter M, et al. Nature Communications 7: 11620
• 2015 Intra-gene DNA Methylation Variability is a Clinically Independent Prognostic Marker in Women's Cancers. Bartlett TE, Widschwendter M, et al. PLoS one, 10(12): e0143178
• 2015 Glioblastoma Stem Cells Respond to Differentiation Cues but Fail to Undergo Commitment and Terminal Cell-Cycle Arrest. Carén H, Stricker SH, Bulstrode H, Gagrica S, Johnstone E, Bartlett TE, Feber A, Wilson G, Teschendorff AE, Bertone P, Beck S, Pollard SM. Stem cell reports, 5(5): 829-842
• 2014 A DNA methylation network interaction measure, and detection of network oncomarkers. Bartlett TE, Olhede SC, Zaikin A. PLoS one, 9(1): e84573
• 2013 Corruption of the intra-gene DNA methylation architecture is a hallmark of cancer. Bartlett TE, Zaikin A, Olhede SC, West J, Teschendorff AE, Widschwendter M. PLoS One, 8(7): e68285
• 2013 A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450K DNA methylation data. Teschendorff AE, Marabita F, Lechner M, Bartlett TE, Tegner J, Gomez-Cabrero D, Beck S. Bioinformatics, 29(2): 189-196

Conference Proceedings

• 2015 A Power Variance Test for Nonstationarity in Complex-Valued Signals. Bartlett TE, Sykulski AC, Olhede SC, Lilly JM, Early JJ. IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 911–916. Preprint available: http://arxiv.org/abs/1508.05593
• 2014 Novel Statistical Network Methodology to Identify and Analyze Cancer  Biomarkers. Bartlett TE, Olhede SC, Zaikin A. Joint Statistical Meeting Proceedings, Statistical Epidemiology Section. American Statistical Association, Boston, MA, U.S.A.

Invited Talks

• 2018 Two-way sparsity for time-varying networks, with applications in genomics. Joint Statistical Meeting. Vancouver, Canada.
• 2017 A dynamic network model for single-cell genomic data. University College London Department of Statistics seminar series. London, U.K.
• 2016 Stochastic network models for `omics applications. Joint Statistical Meeting. Chicago, U.S.A.
• 2015 A Power Variance Test for Nonstationarity in Complex-Valued Signals. IEEE Conference on Machine Learning Applications. Miami, U.S.A.
• 2015 Uni- and Bi-Partite Stochastic Network Models with Applications to 'Omics Data. UC Berkeley Department of Statistics, Statistics and Genomics seminar series. Berkeley, U.S.A.
• 2015 Network inference and community detection, based on covariance matrices, correlations and test statistics from arbitrary distributions. University College London Department of Statistics, Stochastic Processes Group seminar series. London, U.K.
• 2015 Statistical Modelling of Stochastic Processes in Epigenetics. North West Research Associates. Seattle, U.S.A.
• 2014 Statistical Network Methodology for Biomarker Detection. Joint Statistical Meeting. Boston, U.S.A.

Code

File: SBDN_1.0.tar

Description: Implements the sparse Bayesian dynamic network model of Bartlett, et al. (2018), as an R function which calls C++ code.

Usage:

1. Download SBDN_1.0.tar
2. In the terminal, or at the command line or command prompt, navigate to the directory where you downloaded it
3. Type: R CMD INSTALL SBDN_1.0.tar
4. Start R, and load the package by typing require(SBDN)

• The model is implemented as the function dynNetModelSamp
• Syntax and explanation are given in the help file. To view this, type: ?dynNetModelSamp

File: cor2adj.R

Description: Takes as input a correlation matrix estimated from n samples, and outputs an adjacency matrix inferred according to the methodology presented in Bartlett et al. (2016).

Usage: adjMat <- cor2adj(corMat,n)

File: DCSBMcluster.R

Usage: clusters <- DCSBMcluster(adjMat,k,rowNorm,gamma,nrep)

Description: Divides the nodes of a network (as represented by the input adjacency matrix adjMat) into k clusters, by fitting the degree-corrected stochastic blockmodel by spectral clustering, with regularisation and modularity maximisation. Model fitting can sometimes be improved with row-normalisation of the spectral decomposition by setting rowNorm=1. Model fitting can sometimes also sometimes be improved by first clustering only the most important nodes, by setting gamma=1. The k-means step in the spectral clustering is run nrep times, retaining the clustering with the greatest modularity.

File: DCSBMcoCluster.R

Usage: clusters <- DCSBMcoCluster(adjMat,kR,kC,rowNorm,gamma,nrep), where clusters is a list object, containing vectors clustersR and clustersC for the row and column clusters, respectively.

Description: Divides the nodes of a bipartite network (as represented by the asymmetric input adjacency matrix adjMat) into kR row clusters and kC column clusters, by fitting the degree-corrected stochastic co-blockmodel (Bartlett et al., in preparation) by spectral clustering, with regularisation and modularity maximisation. Model fitting can sometimes be improved with row-normalisation of the spectral decomposition by setting rowNorm=1. Model fitting can sometimes also sometimes be improved by first clustering only the most important nodes, by setting gamma=1. The k-means step in the spectral clustering is run nrep times, retaining the clustering with the greatest co-modularity.