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UID:20261001T074612EDT-2026SzI0fH@132.216.98.100
DTSTAMP:20261001T114612Z
DESCRIPTION:Title:\n\nA General Framework for Testing Clustering Significan
 ce and Variable-Level Inference in High-Dimensional Data.\n\nAbstract:\n\n
 Clustering is a fundamental tool for uncovering heterogeneity in data\, ye
 t a longstanding challenge lies in assessing whether detected clusters rep
 resent genuine structure or arise from sampling variability\, and in deter
 mining which variables drive the clustering structure. Statistical signifi
 cance clustering (SigClust\; Liu et al. (2008)) addresses the first challe
 nge by testing the cluster index under a Gaussian null\, estimating its di
 stribution via Monte Carlo simulation in high dimensions. We propose SigCl
 ust-DE\, which builds on recent advances in high-dimensional covariance es
 timation to improve the accuracy of SigClust and extends it to variable-le
 vel inference. In particular\, SigClust-DE unifies clustering significance
  testing and differential expression (DE) analysis\, a central task in RNA
 -seq studies. By leveraging the Monte Carlo framework\, our method control
 s type I error while maintaining high power for variable selection. Throug
 h extensive simulations and an application to RNA-seq data\, we show that 
 SigClust-DE achieves more accurate covariance estimation\, effectively con
 trols false discoveries\, and substantially improves power in detecting di
 fferentially expressed variables\, providing a general framework for clust
 ering significance and variable-level inference in high-dimensional data.
 \n\nSpeaker\n\nHui Shen is a postdoctoral researcher in the Department of 
 Mathematics and Statistics at McGill University. She received her PhD from
  the Department of Statistics and Operations Research at the University of
  North Carolina at Chapel Hill. Her research interests include high-dimens
 ional data analysis\, statistical network analysis\, and differential priv
 acy.\n\nDr. Shen will be online and the presentation will be accessible on
 line using the following Zoom link\; see also the Zoom invitation below\, 
 and retransmitted in Burnside 1104\n\nLocation: In person\, Burnside 1104
 \n\nhttps://mcgill.zoom.us/j/89692052783\n\nMeeting ID: 896 9205 2783\n\nP
 asscode: None\n
DTSTART:20260116T203000Z
DTEND:20260116T213000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Hui Shen (McGill University)
URL:https://www.mcgill.ca/mathstat/channels/event/hui-shen-mcgill-universit
 y-370291
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