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DTSTART;TZID=America/Los_Angeles:20220202T120000
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DTSTAMP:20260517T210027
CREATED:20211216T231745Z
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UID:20174-1643803200-1643805000@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Xinzhou Ge (Li JJ)\, Postdoc in Statistics
DESCRIPTION:TITLE: “P-value-free solution to fix exaggerated false positives by popular differential expression methods.” \nABSTRACT: We report a surprising phenomenon that popular bioinformatics methods for identifying differentially expressed genes (DEG) between two conditions have unexpectedly high false discovery rates (FDRs) on large-sample-size RNA-seq datasets. Failed FDR control is likely due to the invalid p-values which rely on unrealistic assumptions. To address this issue\, we use a general statistical framework Clipper to control the FDR in DEG analysis without relying on p-values. \n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/12/Xinzhou-Ge-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-xinzhou-ge-li-jj-postdoc-in-statistics/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/12/Xinzhou-Ge.jpg
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