BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Institute for Quantitative and Computational Biosciences - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://qcb.ucla.edu
X-WR-CALDESC:Events for Institute for Quantitative and Computational Biosciences
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20200308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20201101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20210314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20211107T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20220313T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20221106T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210507T110000
DTEND;TZID=America/Los_Angeles:20210507T113000
DTSTAMP:20260518T040836
CREATED:20210427T022748Z
LAST-MODIFIED:20210510T164035Z
UID:17994-1620385200-1620387000@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Erin Molloy (Sankararaman)
DESCRIPTION:TITLE: “Advancing admixture graph estimation via maximum likelihood network orientation.” \nABSTRACT: Admixture\, the interbreeding between previously distinct populations\, is a pervasive force in evolution. The evolutionary history of populations in the presence of admixture can be modeled by augmenting phylogenetic trees with additional nodes that represent admixture events. However\, these admixture graphs present formidable inferential challenges. Exhaustively evaluating all admixture graphs can be prohibitively expensive\, so heuristics have been developed to enable efficient search. One heuristic\, implemented in the popular method TreeMix\, consists of adding edges to a starting tree while optimizing a suitable objective function. In this talk\, we will present a demographic model (with one admixed population incident to a leaf) where TreeMix and any other starting-tree-based maximum likelihood heuristic using its likelihood function is guaranteed to get stuck in a local optimum and return an incorrect network topology. We will then demonstrate how this issue can be addressed using our new search strategy: maximum likelihood network orientation (MLNO).\n\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/04/Erin-Molloy-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-erin-molloy-sankararaman/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/png:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/04/erin-molloy.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210507T113000
DTEND;TZID=America/Los_Angeles:20210507T120000
DTSTAMP:20260518T040836
CREATED:20210427T022301Z
LAST-MODIFIED:20210510T164111Z
UID:17990-1620387000-1620388800@qcb.ucla.edu
SUMMARY:QCBio Research Seminar: Ruochen Jiang (Li JJ)
DESCRIPTION:TITLE: “Sources of zeros in single-cell RNA-seq data and how they affect data analysis.” \nABSTRACT: Single-cell RNA sequencing (scRNA-seq) technologies have revolutionized biomedical sciences by enabling genome-wide profiling of gene expression levels at an unprecedented single-cell resolution. A distinct characteristic of scRNA-seq data is the vast proportion of zeros unseen in bulk RNA-seq data. Researchers view these zeros differently: some regard zeros as biological signals representing no or low gene expression\, while others regard zeros as false signals or missing data to be corrected. As a result\, the scRNA-seq field faces much controversy regarding how to handle zeros in data analysis. In this paper\, we first discuss the sources of biological and non-biological zeros in scRNA-seq data. Second\, we summarize the advantages\, disadvantages\, and suitable users of three input data types: original counts\, imputed counts\, and binarized counts. Third\, we evaluate the impacts of non-biological zeros on cell clustering and differential gene expression analysis. Finally\, we discuss the open questions regarding non-biological zeros\, the need for benchmarking\, and the importance of transparent analysis.\nhttps://qcb.ucla.edu/wp-content/uploads/sites/14/2021/04/Ruochen-Jiang-edited.mp4
URL:https://qcb.ucla.edu/event/qcbio-research-seminar-ruochen-jiang-li-jj/
LOCATION:ZOOM\, CA\, United States
CATEGORIES:Research Seminars
ATTACH;FMTTYPE=image/jpeg:https://wp-misc.lifesci.ucla.edu/qcb/wp-content/uploads/sites/14/2021/04/Ruochen-Jiang.jpeg
END:VEVENT
END:VCALENDAR