Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering

This paper proposes an optimised model-free expectation maximisation method for automated clustering of high-dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carriedout using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learningalgorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions arecontinued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometrydatasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters ortheir distribution models

Transcriptional analysis of chIFITM knock-out cell lines

Interferon-inducible transmembrane (IFITM) proteins are restriction factors known to confer antiviral resistance against a wide range of viral pathogens. The chicken IFITM locus is found on Gallus gallus chromosome 5. It contains 4 genes of chIFITM family: chIFITM1, 2, 3, and 5. They have been shown to restrict avian influenza virus in vitro and significantly ameliorate clinical signs in animals in vivo. Here I hypothesise that chIFITM knock-out models could support higher viral titres due to the ablation of chIFITM genes. I develop two models: chIFITM KO DF-1 and chIFITM KO PGC. chIFITM KO DF-1 fibroblasts representing ubiquitous avian fibroblast line used in veterinary research and chIFITM KO PGC modelling an approximation to an animal model. chPGCs are chicken primordial germ cells: precursors of sperm and ova which are very amenable to gene editing and for that reason frequently used for production of transgenic animals in research. Subsequent to gene editing they were differentiated into fibroblasts to facilitate comparison with the DF-1 fibroblasts. The study focussed on the investigation of the effects of the chIFITM KO on the permissiveness of the cells to the influenza virus and their ability to support viral replication. Differential expression analysis and the subsequent pathway analysis was conducted on the differentially expressed genes in native conditions as well as during the influenza infection for both models to compare and contrast differentially regulated pathways for each model

Sparse graphical models for cancer signalling

Protein signalling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. Recent advances in biochemical technology have begun to allow high-throughput, data-driven studies of signalling. In this thesis, we investigate multivariate statistical methods, rooted in sparse graphical models, aimed at probing questions in cancer signalling. First, we propose a Bayesian variable selection method for identifying subsets of proteins that jointly in uence an output of interest, such as drug response. Ancillary biological information is incorporated into inference using informative prior distributions. Prior information is selected and weighted in an automated manner using an empirical Bayes formulation. We present examples of informative pathway and network-based priors, and illustrate the proposed method on both synthetic and drug response data. Second, we use dynamic Bayesian networks to perform structure learning of context-specific signalling network topology from proteomic time-course data. We exploit a connection between variable selection and network structure learning to efficiently carry out exact inference. Existing biology is incorporated using informative network priors, weighted automatically by an empirical Bayes approach. The overall approach is computationally efficient and essentially free of user-set parameters. We show results from an empirical investigation, comparing the approach to several existing methods, and from an application to breast cancer cell line data. Hypotheses are generated regarding novel signalling links, some of which are validated by independent experiments. Third, we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure. Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure. Results are shown from an empirical investigation comparing several penalisation regimes, and an application to breast cancer proteomic data

Sparse graphical models for cancer signalling

Protein signalling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. Recent advances in biochemical technology have begun to allow high-throughput, data-driven studies of signalling. In this thesis, we investigate multivariate statistical methods, rooted in sparse graphical models, aimed at probing questions in cancer signalling. First, we propose a Bayesian variable selection method for identifying subsets of proteins that jointly in uence an output of interest, such as drug response. Ancillary biological information is incorporated into inference using informative prior distributions. Prior information is selected and weighted in an automated manner using an empirical Bayes formulation. We present examples of informative pathwayand network-based priors, and illustrate the proposed method on both synthetic and drug response data. Second, we use dynamic Bayesian networks to perform structure learning of context-specific signalling network topology from proteomic time-course data. We exploit a connection between variable selection and network structure learning to efficiently carry out exact inference. Existing biology is incorporated using informative network priors, weighted automatically by an empirical Bayes approach. The overall approach is computationally efficient and essentially free of user-set parameters. We show results from an empirical investigation, comparing the approach to several existing methods, and from an application to breast cancer cell line data. Hypotheses are generated regarding novel signalling links, some of which are validated by independent experiments. Third, we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure. Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure. Results are shown from an empirical investigation comparing several penalisation regimes, and an application to breast cancer proteomic data.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC)GBUnited Kingdo

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

Identification of sarcoidosis susceptibility genes by association mapping and candidate gene approaches

Identification of a sarcoidosis susceptibility gene on chromosome 6 in this study has been performed using the association mapping approach.Here, a systematic three-stage single nucleotide polymorphism (SNP) scan of 16.4 Mb on chromosome 6p21 was performed in up to 947 independent cases of familial and sporadic sarcoidosis. Using TDT and case-control analyses, a 15 kb segment located at the 3-prime end of the BTNL2 gene could be identified as being strongly associated with sarcoidosis. The major disease-associated variant, rs2076530, represents a risk factor that is entirely independent of the previously reported association between sarcoidosis and alleles of the DRB1 gene, located within ~200kb of BTNL2. BTNL2 is a member of the immunoglobulin superfamily. Homology to B7-1 implicates the BTNL2 as a co-stimulatory molecule. The risk allele A of rs2076530 leads to alternative splicing of the BTNL2 transcript, which introduces a premature stop. The resulting truncated protein lacks the C-terminal IgC domain and transmembrane helix, thereby disturbing the putative co-stimulatory function of this molecule

Immune regulation in multiple myeloma: the host-tumour conflict

The data presented in this thesis demonstrates that the malignant plasma cells of multiple myeloma are capable of suppressing the activation of T lymphocytes. The myeloma cells prevent activation of T cells from healthy donors by allo-antigen, mitogen and IL-2, mediated by the production of soluble, immuno-suppressive factor. This factor was responsible for inducing cell cycle arrest and failure of the T cells to progress into the autocrine IL-2 autocrine pathway, which is of critical importance in the activation of T cells. To further investigate this interaction an in vitro model system was developed to examine the key stages of T cell activation and homeostasis. Myeloma cells constitutively expressed high levels of TGF-1 mRNA transcripts as detected by RT-PCR which were translated into latent protein and secreted as detected by immunohistochemistry and ELSIA, respectively. The reversal of the immuno-suppression induced by the myeloma cells using the specific TGF-1 antagonist, Latency Associated Peptide, confirmed that TGF-1 is a major factor in myeloma-associated suppression of T lymphocyte activation. It was demonstrated that the myeloma cells prevent the T cells, upon activation, from up-regulating the surface expression of the -chain of the IL-2R thus preventing the formation of the high-affinity receptor. The reduced expression of IL-2R resulted from altered transcription of the -chain gene in response to re-stimulation of primary T cells with IL-2. When signalling events in primary T cells responding to re-stimulation with Il-2 was examined, myeloma cells inhibited the phosphorylation of both STAT3 and STAT5. However, using a novel IL-2-dependnet T cell line (IDBL), which does not require the expression of the high affinity IL-2R for its responses to IL-2, it was shown that these cells are insensitive to the myeloma-derived TGF-, in terms of DNA synthesis and proliferation, despite demonstrating failure of phosphorylation of STAT5. It was demonstrated that phosphorylation of STAT3 was unchanged when IDBL cells were co-cultured with myeloma cell lines