seminars:stat:nov162023 [Department of Mathematics and Statistics, Binghamton University]

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<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematics and Statistics</WRAP>

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^  **DATE:**|Thursday, Nov 16, 2023 |
^  **TIME:**|1:15pm -- 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Wenshu Dai, Binghamton University |
^  **TITLE:**|Logistic t-multinomial Clustering for Microbiome Data  |
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<WRAP centeralign>**Abstract**</WRAP>
In the realm of bioinformatics, we frequently encounter discrete data,
particularly microbiome taxa count data obtained through 16S rRNA
sequencing. These microbiome datasets are commonly characterized by their
high dimensionality and the ability to provide insights solely into
relative abundance, necessitating their classification as compositional
data. Analyzing such data presents challenges due to their confinement
within a simplex. Although the multinomial distribution considers the
compositional nature of the data and a Gaussian prior provides flexibility
in modeling covariance matrices, it's important to note that the log-ratio
transformed compositions of microbiome data can exhibit long-tailed
characteristics. Thus, we develop a robust mixture of logistic
t-multinomial models using hierarchical structures of the log-ratio
transformed compositional data, which provide a longer-tailed alternative
to the normal distribution and employs a variational Gaussian approximation
in tandem with the Expectation-Maximization (EM) algorithm for parameter
recovery.
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