Position | Research Associate |

Phone (external) | +44(0)20 3108 3227 |

Phone (internal) | 53227 |

Email(*) | thomas.bartlett.10 |

Themes | Biostatistics, Computational Statistics, Stochastic Modelling and Time Series |

* @ucl.ac.uk

## Research Interests

Stochastic networks and processes; high-dimensional statistics and sparse statistical modelling; 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.

## List of Publications

**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*

## 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:

- Download SBDN_1.0.tar
- In the terminal, or at the command line or command prompt, navigate to the directory where you downloaded it
- Type: R CMD INSTALL SBDN_1.0.tar
- 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.