<div dir="ltr"><div class="gmail_default" style="font-size:small">This webinar is relevant to our research. You are encouraged to attend it. </div><div class="gmail_default" style="font-size:small"><br></div><div class="gmail_default" style="font-size:small">Vivian</div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div style="margin:0px;padding:0px;border:0px;font-stretch:inherit;font-size:12pt;line-height:inherit;font-family:Calibri,Arial,Helvetica,sans-serif;vertical-align:baseline;color:black"><br></div><div style="margin:0px;padding:0px;border:0px;font-stretch:inherit;font-size:12pt;line-height:inherit;font-family:Calibri,Arial,Helvetica,sans-serif;vertical-align:baseline;color:black"></div></div></div></div><br><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">Himel Mallick via American Statistical Association</strong> <span dir="auto"><<a href="mailto:Mail@connectedcommunity.org">Mail@connectedcommunity.org</a>></span><br>Date: Mon, Mar 6, 2023 at 8:32 AM<br>Subject: Section on Statistics in Genomics and Genetics : [ASA SSGG Webinar] Bayesian methods for spatially resolved transcriptomics data analysis<br>To: <<a href="mailto:weil@ucr.edu">weil@ucr.edu</a>><br></div><br><br><u></u>
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<div style="display:none;font-size:1px;color:#333333;line-height:1px;max-height:0px;max-width:0px;opacity:0;overflow:hidden">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 ...</div>
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<a href="https://community.amstat.org/sectiononstatisticsingenomicsandgenetics/discussions/digestviewer?CommunityKey=6a79e11d-ae7b-42ab-9ee9-7bf4fd163d84" target="_blank">Section on Statistics in Genomics and Genetics</a>
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<a href="https://community.amstat.org/sectiononstatisticsingenomicsandgenetics/discussion/asa-ssgg-webinar-bayesian-methods-for-spatially-resolved-transcriptomics-data-analysis#bm2231bbe4-e07c-4c18-936d-2d216a5871a0" style="color:#ffffff" target="_blank">[ASA SSGG Webinar] Bayesian methods for spatially resolved transcriptomics data analysis</a>
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<a href="https://community.amstat.org/members/members/profile?UserKey=b0c5a892-c99c-4a72-92b1-c6324ed1386b" target="_blank"><img alt="Himel Mallick" title="Himel Mallick" src="https://d132x6oi8ychic.cloudfront.net/higherlogic/directory/imagedisplay/4f1b7f4e-5e8d-4f42-ae93-a62d3774d49c/4cad3218-eec3-4757-b56c-3702a8298a29/200/200/637872503135930000" border="0" width="75"></a></td>
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Mar 6, 2023 11:33 AM <span style="color:#999999">|</span> <img alt="attachment" border="0" src="https://d2x5ku95bkycr3.cloudfront.net/App_Themes/Common/images/eGroups/handouts.gif" title="attachment" height="12" width="12" style="opacity:0.5;opacity:alpha(opacity=50)"> <a href="https://community.amstat.org/sectiononstatisticsingenomicsandgenetics/viewdocument/asa-ssgg-webinar-bayesian-methods?CommunityKey=6a79e11d-ae7b-42ab-9ee9-7bf4fd163d84&tab=librarydocuments&MessageKey=2231bbe4-e07c-4c18-936d-2d216a5871a0" style="color:#336699;display:inline-block;padding-bottom:10px;padding-left:5px;padding-right:5px" target="_blank">view attached</a>
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<a href="https://community.amstat.org/members/members/profile?UserKey=b0c5a892-c99c-4a72-92b1-c6324ed1386b" target="_blank">Himel Mallick</a>
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<p><strong><span>Webinar on "</span><span>Bayesian methods for spatially resolved transcriptomics data analysis</span><span>"</span></strong></p>
<p><strong><span>03/27/2023 | 2:00 - 3:00 PM (Eastern) / 11:00 AM - 12:00 PM (Pacific)</span></strong></p>
<p><span><strong>Registration (free but required)</strong>: </span><a href="https://zoom.us/webinar/register/WN_U_1nx442Qmu7_oZAx0Cyyg" title="https://zoom.us/webinar/register/WN_U_1nx442Qmu7_oZAx0Cyyg" target="_blank"><span>https://zoom.us/webinar/register/WN_U_1nx442Qmu7_oZAx0Cyyg</span></a></p>
<p><strong><span>Abstract:</span></strong></p>
<p><span>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.</span></p>
<p><strong><span>Speaker Bio: </span></strong></p>
<p><strong><span>Qiwei Li</span></strong><span><strong>, Ph.D.</strong>, </span><span>Assistant Professor</span><span>, </span><span>Department of Mathematical Sciences at the University of Texas at Dallas (UTD</span><span></span></p>
<p><span>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.</span><span></span></p>
<p><span>A flyer is attached.</span></p><br><br>------------------------------<br>Himel Mallick, PhD<br>Associate Principal Scientist<br>Biostatistics<br>Merck Research Laboratories<br>Rahway, NJ 07065<br>Web: <a href="http://himelmallick.org" target="_blank">himelmallick.org</a><br>Pronouns: he/him/his<br>------------------------------<br>
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