Dr Thomas Bartlett

* @ucl.ac.uk

Research Interests

Computational Statistics, Networks, Multivariate and High Dimensional Data, Genomics, Mathematical Biology

Track record at UCL

• 2023 Appointed associate editor of the Journal of the Royal Statistical Society (series C)
• 2023 Appointed fellow of the Higher Education Academy (FHEA).
• 2020 Appointed as lecturer by University College London (open-ended contract).
• 2017 MRC Fellowship (skills development, quantitative). Awarded by U.K. research council, to fund three years’ independent postdoctoral 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.
• 2010 Studentship awarded by University College London, to fully fund 4 years of doctoral training, via the UCL CoMPLEX DTC.

Teaching at UCL

• 2023-2024 STAT0032 Introduction to Statistical Data Science.
• 2022-2023 STAT0017 Statistical Genomics. Download the course materials here.
• 2021-2022 STAT0030 Statistical Computing.
• 2021-2022 STAT0022 Introductory Statistical Methods.

List of Publications

• 2023 Stochastic networks theory to model single-cell genomic count data. Bartlett TE, et al. Submitted. Preprint available: http://arxiv.org/abs/2303.02498
• 2023 Natural killer cell dysfunction in premenopausal BRCA1 mutation carriers: a mechanism for ovarian carcinogenesis. Haran S, ..., Bartlett TE, et al Submitted.
• 2023 MICA: A multi-omics method to predict gene regulatory networks in early human embryos. Alanis-Lobato G*, Bartlett TE*, Huang Q*, et al. *Joint first authors. Life Science Alliance 7(1):  e202302415. https://doi.org/10.26508/lsa.202302415
• 2022 Antiprogestins reduce epigenetic field cancerization in breast tissue of young healthy women. Bartlett TE, et al. Genome Medicine 14(1): 1-18. https://doi.org/10.1186/s13073-022-01063-5
• 2022 Susceptibility to hormone-mediated cancer is reflected by different tick rates of the epithelial and general epigenetic clock. Barrett JE, …, Bartlett TE, et al. Genome Biology 23(1): 1-16. https://doi.org/10.1186/s13059-022-02603-3
• 2021 Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature. Bartlett TE, et al. Nature Scientific Reports 11(1): 23702. https://www.nature.com/articles/s41598-021-03161-7
• 2021 Two-way sparsity for time-varying networks, with applications in genomics. Bartlett TE, Kosmidis I, Silva R. Annals of Applied Statistics 15(2): 856-879. http://arxiv.org/abs/1802.08114
• 2021 Fusion of single-cell transcriptome and DNA-binding data, for genomic network inference in cortical development. Bartlett TE. BMC Bioinformatics 22(1):1-9. https://www.biorxiv.org/content/10.1101/2021.05.18.444638
• 2021 Co-modularity and co-community detection in large networks. Bartlett TE. Physical Review E, 104(5): 054309. http://arxiv.org/abs/1511.05611
• 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. 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.