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Search Results: 1 - 10 of 10019 matches for " Simon Tavaré "
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Sparse Partitioning: Nonlinear regression with binary or tertiary predictors, with application to association studies
Doug Speed,Simon Tavaré
Quantitative Biology , 2011, DOI: 10.1214/10-AOAS411
Abstract: This paper presents Sparse Partitioning, a Bayesian method for identifying predictors that either individually or in combination with others affect a response variable. The method is designed for regression problems involving binary or tertiary predictors and allows the number of predictors to exceed the size of the sample, two properties which make it well suited for association studies. Sparse Partitioning differs from other regression methods by placing no restrictions on how the predictors may influence the response. To compensate for this generality, Sparse Partitioning implements a novel way of exploring the model space. It searches for high posterior probability partitions of the predictor set, where each partition defines groups of predictors that jointly influence the response. The result is a robust method that requires no prior knowledge of the true predictor--response relationship. Testing on simulated data suggests Sparse Partitioning will typically match the performance of an existing method on a data set which obeys the existing method's model assumptions. When these assumptions are violated, Sparse Partitioning will generally offer superior performance.
Assessing molecular variability in cancer genomes
A. D. Barbour,Simon Tavaré
Quantitative Biology , 2010,
Abstract: The dynamics of tumour evolution are not well understood. In this paper we provide a statistical framework for evaluating the molecular variation observed in different parts of a colorectal tumour. A multi-sample version of the Ewens Sampling Formula forms the basis for our modelling of the data, and we provide a simulation procedure for use in obtaining reference distributions for the statistics of interest. We also describe the large-sample asymptotics of the joint distributions of the variation observed in different parts of the tumour. While actual data should be evaluated with reference to the simulation procedure, the asymptotics serve to provide theoretical guidelines, for instance with reference to the choice of possible statistics.
Modeling Evolutionary Dynamics of Epigenetic Mutations in Hierarchically Organized Tumors
Andrea Sottoriva ,Louis Vermeulen ,Simon Tavaré
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1001132
Abstract: The cancer stem cell (CSC) concept is a highly debated topic in cancer research. While experimental evidence in favor of the cancer stem cell theory is apparently abundant, the results are often criticized as being difficult to interpret. An important reason for this is that most experimental data that support this model rely on transplantation studies. In this study we use a novel cellular Potts model to elucidate the dynamics of established malignancies that are driven by a small subset of CSCs. Our results demonstrate that epigenetic mutations that occur during mitosis display highly altered dynamics in CSC-driven malignancies compared to a classical, non-hierarchical model of growth. In particular, the heterogeneity observed in CSC-driven tumors is considerably higher. We speculate that this feature could be used in combination with epigenetic (methylation) sequencing studies of human malignancies to prove or refute the CSC hypothesis in established tumors without the need for transplantation. Moreover our tumor growth simulations indicate that CSC-driven tumors display evolutionary features that can be considered beneficial during tumor progression. Besides an increased heterogeneity they also exhibit properties that allow the escape of clones from local fitness peaks. This leads to more aggressive phenotypes in the long run and makes the neoplasm more adaptable to stringent selective forces such as cancer treatment. Indeed when therapy is applied the clone landscape of the regrown tumor is more aggressive with respect to the primary tumor, whereas the classical model demonstrated similar patterns before and after therapy. Understanding these often counter-intuitive fundamental properties of (non-)hierarchically organized malignancies is a crucial step in validating the CSC concept as well as providing insight into the therapeutical consequences of this model.
Human hair genealogies and stem cell latency
Jung Kim, Simon Tavaré, Darryl Shibata
BMC Biology , 2006, DOI: 10.1186/1741-7007-4-2
Abstract: To test this approach, epigenetic errors (methylation) in CpG-rich molecular clocks were measured from human hairs. Hairs exhibit growth and replacement cycles and "new" hairs physically reappear even on "old" heads. Errors may accumulate in long-lived stem cells, or in their differentiated progeny that are eventually shed.Average hair errors increased until two years of age, and then were constant despite decades of replacement, consistent with new hairs arising from infrequently dividing bulge stem cells. Errors were significantly more frequent in longer hairs, consistent with long-lived but eventually shed mitotic follicle cells.Constant average hair methylation regardless of age contrasts with the age-related methylation observed in human intestine, suggesting that error accumulation and therefore stem cell latency differs among tissues. Epigenetic molecular clocks imply similar mitotic ages for hairs on young and old human heads, consistent with a restart with each new hair, and with genealogies surreptitiously written within somatic cell genomes.One way to organize the billions of cells within an individual is through genealogy, because all cells are related. Each cell has its own genealogy, which starts from the zygote and ends with the current phenotype. Conceptually, the genealogy of a differentiated epithelial cell can be divided into three distinct phases: neogenesis or development between the zygote and tissue formation, stem cell latency, and differentiation (Figure 1). Stem cells are the progenitors or common ancestors of the much larger numbers of differentiated cells that compose our bodies. The stem cell phase is chronologically the longest because developing and differentiated cells survive for relatively short periods. However, it is uncertain how often stem cells divide because they are rare, difficult to culture, and lack unique identifying markers.Stem cells are few in number, but it is possible to infer how often they divide by measuring the g
Hypothesis Testing for the Covariance Matrix in High-Dimensional Transposable Data with Kronecker Product Dependence Structure
Anestis Touloumis,John Marioni,Simon Tavaré
Statistics , 2014,
Abstract: The matrix-variate normal distribution is a popular model for high-dimensional transposable data because it decomposes the dependence structure of the random matrix into the Kronecker product of two covariance matrices: one for each of the row and column variables. We develop tests for assessing the form of the row (column) covariance matrix in high-dimensional settings while treating the column (row) dependence structure as a nuisance. Our tests are robust to normality departures provided that the Kronecker product dependence structure holds. In simulations, we observe that the proposed tests maintain the nominal level and are powerful against the alternative hypotheses tested. We illustrate the utility of our approach by examining whether genes associated with a given signalling network show correlated patterns of expression in different tissues and by studying correlation patterns within measurements of brain activity collected using electroencephalography.
Testing the Mean Matrix in High-Dimensional Transposable Data
Anestis Touloumis,Simon Tavaré,John C. Marioni
Statistics , 2014, DOI: 10.1111/biom.12257
Abstract: The structural information in high-dimensional transposable data allows us to write the data recorded for each subject in a matrix such that both the rows and the columns correspond to variables of interest. One important problem is to test the null hypothesis that the mean matrix has a particular structure without ignoring the potential dependence structure among and/or between the row and column variables. To address this, we develop a simple and computationally efficient nonparametric testing procedure to assess the hypothesis that, in each predefined subset of columns (rows), the column (row) mean vector remains constant. In simulation studies, the proposed testing procedure seems to have good performance and unlike traditional approaches, it is powerful without leading to inflated nominal sizes. Finally, we illustrate the use of the proposed methodology via two empirical examples from gene expression microarrays.
The Stem Cell Population of the Human Colon Crypt: Analysis via Methylation Patterns
Pierre Nicolas ,Kyoung-Mee Kim,Darryl Shibata,Simon Tavaré
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030028
Abstract: The analysis of methylation patterns is a promising approach to investigate the genealogy of cell populations in an organism. In a stem cell–niche scenario, sampled methylation patterns are the stochastic outcome of a complex interplay between niche structural features such as the number of stem cells within a niche and the niche succession time, the methylation/demethylation process, and the randomness due to sampling. As a consequence, methylation pattern studies can reveal niche characteristics but also require appropriate statistical methods. The analysis of methylation patterns sampled from colon crypts is a prototype of such a study. Previous analyses were based on forward simulation of the cell content of the whole crypt and subsequent comparisons between simulated and experimental data using a few statistics as a proxy to summarize the data. In this paper we develop a more powerful method to analyze these data based on coalescent modelling and Bayesian inference. Results support a scenario where the colon crypt is maintained by a high number of stem cells; the posterior indicates a number greater than eight and the posterior mode is between 15 and 20. The results also provide further evidence for synergistic effects in the methylation/demethylation process that could for the first time be quantitatively assessed through their long-term consequences such as the coexistence of hypermethylated and hypomethylated patterns in the same colon crypt.
High DNA Methylation Pattern Intratumoral Diversity Implies Weak Selection in Many Human Colorectal Cancers
Kimberly D. Siegmund, Paul Marjoram, Simon Tavaré, Darryl Shibata
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0021657
Abstract: Background It is possible to infer the past of populations by comparing genomes between individuals. In general, older populations have more genomic diversity than younger populations. The force of selection can also be inferred from population diversity. If selection is strong and frequently eliminates less fit variants, diversity will be limited because new, initially homogeneous populations constantly emerge. Methodology and Results Here we translate a population genetics approach to human somatic cancer cell populations by measuring genomic diversity within and between small colorectal cancer (CRC) glands. Control tissue culture and xenograft experiments demonstrate that the population diversity of certain passenger DNA methylation patterns is reduced after cloning but subsequently increases with time. When measured in CRC gland populations, passenger methylation diversity from different parts of nine CRCs was relatively high and uniform, consistent with older, stable lineages rather than mixtures of younger homogeneous populations arising from frequent cycles of selection. The diversity of six metastases was also high, suggesting dissemination early after transformation. Diversity was lower in DNA mismatch repair deficient CRC glands, possibly suggesting more selection and the elimination of less fit variants when mutation rates are elevated. Conclusion/Significance The many hitchhiking passenger variants observed in primary and metastatic CRC cell populations are consistent with relatively old populations, suggesting that clonal evolution leading to selective sweeps may be rare after transformation. Selection in human cancers appears to be a weaker than presumed force after transformation, consistent with the observed rarity of driver mutations in cancer genomes. Phenotypic plasticity rather than the stepwise acquisition of new driver mutations may better account for the many different phenotypes within human tumors.
BayesPeak: Bayesian analysis of ChIP-seq data
Christiana Spyrou, Rory Stark, Andy G Lynch, Simon Tavaré
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-299
Abstract: Our proposed statistical algorithm, BayesPeak, uses a fully Bayesian hidden Markov model to detect enriched locations in the genome. The structure accommodates the natural features of the Solexa/Illumina sequencing data and allows for overdispersion in the abundance of reads in different regions. Moreover, a control sample can be incorporated in the analysis to account for experimental and sequence biases. Markov chain Monte Carlo algorithms are applied to estimate the posterior distributions of the model parameters, and posterior probabilities are used to detect the sites of interest.We have presented a flexible approach for identifying peaks from ChIP-seq reads, suitable for use on both transcription factor binding and histone modification data. Our method estimates probabilities of enrichment that can be used in downstream analysis. The method is assessed using experimentally verified data and is shown to provide high-confidence calls with low false positive rates.The importance of DNA-binding proteins in molecular functions such as transcription, replication, DNA repair and chromosome segregation highlights the significance of identifying the locations of their binding sites throughout the genome. The most widely used method for mapping these genomic locations is chromatin immunoprecipitation (ChIP). This process involves shearing the DNA and isolating the fragments to which proteins have bound [1], after which various methods can be used to identify those protein-bound fragments. A similar approach may be used to identify histone marks such as trimethylation. Direct sequencing is a reliable and efficient technique that is gradually replacing microarray hybridization for determining the contents of the immunoprecipitated samples [2]. These two procedures are widely known as ChIP-seq and ChIP-chip respectively, and both present their own statistical challenges. Hidden Markov models (HMM) fit naturally in this framework and have had numerous implementations in the
Simultaneous tracking of movement and gene expression in multiple Drosophila melanogaster flies using GFP and DsRED fluorescent reporter transgenes
Dhruv Grover, Junsheng Yang, Daniel Ford, Simon Tavaré, John Tower
BMC Research Notes , 2009, DOI: 10.1186/1756-0500-2-58
Abstract: Here we report an improved fly video tracking system that allows multiple transgenic flies to be tracked simultaneously using visible light, GFP fluorescence and DsRED fluorescence. The movement of multiple flies could be accurately tracked at real time rates, while simultaneously assaying the expression level of two different transgenes marked with GFP and DsRED. The individual flies could be accurately tracked and distinguished even during periods when transgene fluorescence was undetected. For example, characteristic patterns of hsp70 and hsp22 transgene induction could be simultaneously quantified and correlated with animal movement in aging flies, and as groups of flies died due to dessication/starvation.The improved methods allow for more efficient assay of the correlation between gene expression, behavior, aging and mortality: multiple animals can be assayed with simultaneous quantification of multiple transgenes using GFP and DsRED fluorescence. These methods should allow for increased flexibility in experimental designs. For example, in the future it should be possible to use gene expression levels to predict remaining life span more accurately, and to quantify gene expression changes caused by interactions between animals in real time.The use of autofluorescent proteins such as Green Fluorescent Protein (GFP) has caused a revolution in biology and the study of gene expression [1,2]. Because the proteins require no added substrate in order to fluoresce, they require no invasive procedures for their assay, and serve as ideal reporters for the expression of transgenes in cells and whole animals. In addition, protein activity, protein-protein interactions, and protein sub-cellular locations can be assayed using fluorescence [3,4].Video tracking is increasingly being used in studies of movement, behavior, and aging across species [5-8]. Examples include detailed studies of movement behavior in Drosophila[9,10] and C. elegans [11]. We have recently reported a vi
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