[Lilab] Fwd: Section on Statistics in Genomics and Genetics : [ASA SSGG Webinar] Bayesian methods for spatially resolved transcriptomics data analysis

[Wei Vivian Li]

This webinar is relevant to our research. You are encouraged to attend it.

Vivian



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From: Himel Mallick via American Statistical Association <
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Date: Mon, Mar 6, 2023 at 8:32 AM
Subject: Section on Statistics in Genomics and Genetics : [ASA SSGG
Webinar] Bayesian methods for spatially resolved transcriptomics data
analysis
To: <weil at ucr.edu>


Webinar on "Bayesian methods for spatially resolved transcriptomics data
analysis" 03/27/2023 | 2:00 - 3:00 PM (Eastern) / 11:00 AM - 12:00 PM ...
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Himel Mallick
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*Webinar on "Bayesian methods for spatially resolved transcriptomics data
analysis"*

*03/27/2023 | 2:00 - 3:00 PM (Eastern) / 11:00 AM - 12:00 PM (Pacific)*

*Registration (free but required)*:
https://zoom.us/webinar/register/WN_U_1nx442Qmu7_oZAx0Cyyg

*Abstract:*

The location, timing, and abundance of gene expression within a tissue
define the molecular mechanisms of cell functions. Recent technology
breakthroughs in spatial molecular profiling, including imaging-based
technologies and sequencing-based technologies, have enabled the
comprehensive molecular characterization of single cells while preserving
their spatial and morphological contexts. This new bioinformatics scenario
calls for effective and robust computational methods to identify genes with
spatial patterns. Before discussing some recent advances in spatially
resolved transcriptomics data analysis using Bayesian approaches, I will
introduce two novel Bayesian hierarchical models to detect spatially
variable genes. The first model based on Gaussian process directly models
the zero-inflated and over-dispersed counts. The second model based on
Ising model uses the energy interaction parameter to characterize a
denoised spatial pattern. The Bayesian inference framework allows us to
borrow strength in parameter estimation in a de novo fashion. The two
proposed models show competitive performances in accuracy and robustness
over existing methods in both simulation studies and two real data
applications.

*Speaker Bio: *

*Qiwei Li**, Ph.D.*, Assistant Professor, Department of Mathematical
Sciences at the University of Texas at Dallas (UTD

Dr. Qiwei Li is an Assistant Professor in the Department of Mathematical
Sciences at the University of Texas at Dallas (UTD). Before joining UTD in
2019, he was an Assistant Professor at the University of Texas Southwestern
Medical Center, where he also received his postdoc training. Dr. Li
received his Ph.D. from the Department of Statistics at Rice University in
2016 under the supervision of Dr. Marina Vannucci. Dr. Li has been actively
involved in developing Bayesian methodology to address critical biomedicine
issues. Dr. Li has been very productive in high-dimensional count data
modeling, spatial analysis, and shape analysis. The developed methods have
been used to analyze spatial transcriptomics data, microbiome data, and
artificial intelligence-reconstructed medical imaging data. He has over 40
publications in top impact peer-reviewed journals such as the Annals of
Applied Statistics, Biometrics, Biostatistics, Bioinformatics, and more in
the past five years since he started his academic career. His research is
also recognized by government agencies, including NIH and NSF, where he is
serving as PI and co-PI for many grants.

A flyer is attached.


------------------------------
Himel Mallick, PhD
Associate Principal Scientist
Biostatistics
Merck Research Laboratories
Rahway, NJ 07065
Web: himelmallick.org
Pronouns: he/him/his
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