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UID:20250312T161309EDT-31497Z91Op@132.216.98.100
DTSTAMP:20250312T201309Z
DESCRIPTION:Yusha Liu\, PhD\n\nAssistant Professor\n	Department of Biostatis
 tics | UNC-Chapel Hill\n	\n	WHEN: Wednesday\, March 26\, 2025\, from 3:30 to
  4:30 p.m.\n	WHERE: Hybrid | 2001 9IÖÆ×÷³§Ãâ·Ñ College Avenue\, Room 1140\; Zoom
 \n	NOTE: Yusha Liu will be presenting from Chapel Hill\n\nAbstract\n\nProfi
 ling tumors with single-cell RNA sequencing has the potential to identify 
 recurrent patterns of transcription variation related to cancer progressio
 n\, and to produce therapeutically relevant insights. However\, strong int
 er-tumor heterogeneity can obscure more subtle patterns that are shared ac
 ross tumors. In this talk\, I will introduce a novel statistical method\, 
 generalized binary covariance decomposition (GBCD)\, to address this probl
 em. GBCD can decompose transcriptional heterogeneity into interpretable co
 mponents—including patient-specific\, dataset-specific and shared componen
 ts relevant to disease subtypes—and that\, in the presence of strong inter
 -tumor heterogeneity\, it can produce more interpretable results than exis
 ting methods. Applied to data on pancreatic ductal adenocarcinoma\, GBCD p
 roduced a refined characterization of existing tumor subtypes\, and identi
 fied a gene expression program prognostic of poor survival independent of 
 tumor stage and subtype. The gene expression program is enriched for genes
  involved in stress responses\, and suggests a role for the integrated str
 ess response in pancreatic ductal adenocarcinoma.\n\nSpeaker Bio\n\nDr. Yu
 sha Liu is a research assistant professor at the Department of Biostatisti
 cs at the University of North Carolina at Chapel Hill. She received her Ph
 D in Statistics from Rice University and postdoctoral training from the De
 partment of Human Genetics at the University of Chicago. Dr. Liu’s researc
 h interests lie at the intersection of statistics and cancer biology\, and
  she is particularly interested in developing and applying flexible and sc
 alable statistical approaches to analyzing large-scale and complex genomic
 s data\, such as single cell data\, and ultimately contributing to the und
 erstanding of cancer etiology and the development of effective prevention 
 strategies and targeted therapies.\n
DTSTART:20250326T193000Z
DTEND:20250326T203000Z
SUMMARY:Dissecting Tumor Transcriptional Heterogeneity from Single-cell RNA
 -Seq Data by Generalized Binary Covariance Decomposition
URL:/spgh/channels/event/dissecting-tumor-transcriptio
 nal-heterogeneity-single-cell-rna-seq-data-generalized-binary-363760
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