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UID:DSC-18267
DTSTART;TZID=Europe/Berlin:20211130T160000
SEQUENCE:1636724600
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20211130T170000
URL:https://dresden-science-calendar.org/calendar/de/detail/18267
LOCATION:Online\,   
SUMMARY:He: Liang He: NEBULA (Single Cell Seminars)
CLASS:PUBLIC
DESCRIPTION:Speaker: Liang He\nInstitute of Speaker: Duke University\nTopic
 s:\nMathematik\, Informatik\n Location:\n  Name: Online (via Zoom)\n  Stre
 et:  \n  City:  \n  Phone: \n  Fax: \nDescription: Liang He: NEBULA: a fas
 t negative binomial mixed model for differential analysis of large-scale m
 ulti-subject single-cell data (Single Cell Seminars)    Large-scale multi-
 subject single-cell data become increasingly available in recent years. Ac
 curate and efficient differential analysis plays a pivotal role in identif
 ying marker genes\, detecting biologically relevant effects\, performing c
 o-expression analysis\, and generating normalized data for downstream anal
 ysis including clustering. Negative binomial mixed models (NBMMs)\, accoun
 ting for both cell-level and subject-level overdispersions\, are an ideal 
 model for handling such an intrinsic hierarchical structure. However\, NBM
 Ms are very computationally demanding. In this talk\, we introduce an effi
 cient NEgative Binomial mixed model Using a Large-sample Approximation (NE
 BULA). NEBULA is orders of magnitude faster than existing tools and contro
 ls false-positive errors in marker gene identification and co-expression a
 nalysis. The speed gain is achieved by analytically solving high-dimension
 al integrals instead of using the Laplace approximation. Using NEBULA in A
 lzheimer’s disease cohort data sets\, we found that the cell-level expre
 ssion of APOE correlated with that of other genetic risk factors (includin
 g CLU\, CST3\, TREM2\, C1q\, and ITM2B) in a cell-type-specific pattern an
 d an isoform-dependent manner in microglia. NEBULA opens up a new avenue f
 or the broad application of mixed models to large-scale multi-subject sing
 le-cell data.
DTSTAMP:20260420T170539Z
CREATED:20211112T131815Z
LAST-MODIFIED:20211112T134320Z
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