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UID:20260804T055614EDT-81198ltJf6@132.216.98.100
DTSTAMP:20260804T095614Z
DESCRIPTION:Abstract: The capability of flow cytometry to offer rapid\nquan
 tification of multidimensional characteristics for millions of\ncells has 
 made this technology indispensable for health research\,\nmedical diagnosi
 s\, and treatment. However\, the lack of statistical\nand bioinformatics t
 ools to parallel recent high-throughput\ntechnological advancements has hi
 ndered this technology from\nreaching its full potential.\nWe propose a fl
 exible statistical model-based clustering\napproach for identifying cell p
 opulations in flow cytometry data\nbased on t mixture models with a Box-Co
 x transformation. This\napproach generalizes the popular Gaussian mixture 
 models to account\nfor outliers and allow for non- elliptical clusters. We
  describe an\nExpectation- Maximization (EM) algorithm to simultaneously h
 andle\nparameter estimation and transformation selection.\nUsing two publi
 cly available datasets\, we demonstrate that our\nproposed methodology pro
 vides enough flexibility and robustness to\nmimic manual gating results pe
 rformed by an expert researcher. The\nproposed clustering methodology is w
 ell-adapted to automated\nanalysis of flow cytometry data. It tends to giv
 e more reproducible\nresults\, and helps reduce the significant subjectivi
 ty and human\ntime cost encountered in manual gating analysis.\n
DTSTART:20081210T193000Z
DTEND:20081210T210000Z
LOCATION:McIntyre Medical Building\, CA\, QC\, Montreal\, H3G 1Y6\, 3655 pr
 omenade Sir William Osler
SUMMARY:Dr. Raphael Gottardo\, IRCM\, Montreal 'Automated Gating of Flow Cy
 tometry Data via Robust Model-Based Clustering'
URL:/channels/event/dr-raphael-gottardo-ircm-montreal-
 automated-gating-flow-cytometry-data-robust-model-based-clustering-102784
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