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Caros: <br>
gostaria de convidá-los para o seminário desta quinta-feira com o Carlos Zanini da UFRJ.</div>
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Vos aguardo lá.</div>
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Abs</div>
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<b><span style="font-size:13.5pt; line-height:105%; font-family:"Arial",sans-serif; color:#1F4E79">DEPENDENT MIXTURES: MODELLING CELL LINEAGES</span></b><b><span style="font-size:13.5pt; line-height:105%; font-family:"Arial",sans-serif; color:#2F5597">
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<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Cell lineage data comes from single-cell transcriptomics and it is used to recover the evolutionary path of cells in a given environment. The different evolutionary
 stages of the cells can be probabilistically described by distinct components in a mixture model. This work proposes a Bayesian dependent mixture model where the dependence on the components of the mixture explicitly incorporates the biological structure that
 characterizes cell lineage applications. We use a random tree structure (Minimum Spanning Tree) not only to explain the snapshot in the latent space of the continuous development of cells from its initial stage into mature differentiated cells, but also to
 model the dependence structure between the clusters of cells. Regularization is incorporated in the form of a prior penalization on trees with too many nodes or with redundant edges. Consequently, the model assumes the partition of cells to depend on the lineage
 structure, which is more biologically reasonable then the usual multistep approach in which partitions are estimated disregarding the underlying tree structure that characterizes cell lineage data. We are able to provide full inference (with uncertainty captured
 by the posterior samples obtained through MCMC) on the clusters of cells (including number of clusters), on the underlying tree structure and also on pseudotimes. (Authors: Zanini, C. T. P, Paulon, G., Mueller, P.).</span><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#4785D1"></span></p>
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<b><sup><span style="font-family:"Arial",sans-serif; color:#3B3838">                                                       </span></sup></b><b><sup><span style="font-family:"Arial",sans-serif; color:#1F497D"> 
</span></sup></b><b><sup><span style="font-family:"Arial",sans-serif; color:#3B3838">                                              </span></sup></b><sup><span style="font-size:9.0pt; font-family:"Arial",sans-serif; color:#595959">Texto informado pelo autor</span></sup><b><sup><span style="font-size:9.0pt; font-family:"Arial",sans-serif; color:#3B3838">.</span></sup></b><span style="font-family:"Arial",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif; color:#1F4E79">Quando:</span></b><span style="font-family:"Arial",sans-serif"></span></p>
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<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">23 de Julho de 2020, às
<b>16h</b></span></p>
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<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Via Zoom</span><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif"></span></p>
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<span style="font-size:10.5pt; font-family:"Arial",sans-serif; color:#595959">Registre-se com antecedência para este seminário:
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<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Após o registro, você receberá um e-mail de confirmação contendo informações sobre conexão no seminário.</span><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif; color:#1F4E79">Palestrante:</span></b><b><span style="font-family:"Arial",sans-serif"></span></b></p>
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<b><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Carlos Zanini</span></b><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959"> is assistant professor in Departamento
 de Métodos Estatísticos (Department of Statistical Methods) at Universidade Federal do Rio de Janeiro (Federal University of Rio de Janeiro), Brazil. He earned his PhD (2019) from the Statistics and Data Sciences department of University of Texas at Austin,
 advised by professors Peter Müller and Mingyuan Zhou. He also holds MSc and BSc degrees in statistics from UFRJ. His main research interests are Bayesian Statistics, Biostatistics, Mixture models, Bayesian Non-Parametrics, Dynamic Models, Deep Learning and
 Neural Networks.</span><span style="font-size:12.0pt; line-height:115%; font-family:"Arial",sans-serif"></span></p>
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