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--- seminars:stat:200423 [2020/04/21 14:17] – qyu
+++ seminars:stat:200423 [2020/04/22 16:12] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, April 23, 2020 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|zoom meeting |
+^  **SPEAKER:**|Yuan Fang, Binghamton  University |
+^  **TITLE:**|Variational inference of logistic-normal multinomial mixture model for clustering microbiome data |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+Discrete data, for one example, the microbiome taxa count data
+resulting from 16S rRNA sequencing, is routinely encountered in
+bioinformatics. Taxa count data in microbiome studies are typically
+high-dimensional, overdispersed, and can only reveal relative abundance,
+therefore treated compositional. Analyzing compositional data presents many
+challenges because they are restricted on a simplex.  Through
+logistic-normal multinomial model, one can map the relative abundance from
+a simplex to a latent variable that exists on the real Euclidean space
+using the additive log-ratio transformation.  While this approach based on
+latent variables brings in flexibility in modeling the data, it comes with
+heavy computational cost when implemented relying on Bayesian techniques.
+In this paper, we extend the logistic-normal multinomial transformation in
+a finite mixture model framework to cluster microbiome data. Variational EM
+based on the variational Gaussian approximations (VGA), where complex
+posterior distributions are approximated using computationally convenient
+Gaussian densities by minimizing the Kullback-Leibler (KL) divergence
+between the true and the approximating densities of the posterior of the
+latent variables, were utilized for parameter estimation. Adopting a
+variational Gaussian approximation delivers accurate approximations of the
+complex posterior while reducing computational overhead substantially. The
+proposed method provides favorable clustering results and accurate
+parameter recovery, which are illustrated using simulation and real data.
+</WRAP>