BEGIN:VCALENDAR

VERSION:2.0

PRODID:-//wp-events-plugin.com//7.2.2.1//EN

TZID:Asia/Jerusalem

X-WR-TIMEZONE:Asia/Jerusalem
BEGIN:VEVENT

UID:1163@biology.technion.ac.il

DTSTART;TZID=Asia/Jerusalem:20230208T130000

DTEND;TZID=Asia/Jerusalem:20230208T133000

DTSTAMP:20230129T135244Z

URL:https://biology.technion.ac.il/en/seminars/msc-graduate-seminar-mahmou
 d-yazbak/

SUMMARY:Msc Graduate Seminar-Mahmoud Yazbak [No Categories]
DESCRIPTION:Location: hybrid- in the Faculty Auditorium/ZOOM: https://techn
 ion.zoom.us/j/91959760710   Mahmoud Yazbak\n Affiliation: \n Host:Dr. Aran
  Dvir\n Reference-based deep examination of single-cell RNA-seq clusters\n
 \nIdentifying the biological state of each single cell is arguably the mos
 t challenging task in scRNA-seq data analysis. Doing so requires bridging 
 the gap between the current dataset and prior biological knowledge\, and t
 he latter is not always available in a consistent manner. To improve this 
 process\, we've developed reference datasets for mouse and human containin
 g over 60\,000 samples of over 200 cell types each\, from publicly availab
 le studies. Using these reference datasets\, we've developed a refined ver
 sion of our previously published scRNA-seq annotation tool SingleR\, which
  allows us to discover new findings within scRNA-seq datasets\, such as ce
 ll type annotations\, errors made in previous annotation and clustering at
 tempts\, subtypes or substates of cells and clusters\, identification of r
 are or diseased cell types\, and more. 
LOCATION:hybrid- in the Faculty Auditorium/ZOOM: https://technion.zoom.us/j
 /91959760710 

END:VEVENT

BEGIN:VTIMEZONE

TZID:Asia/Jerusalem

X-LIC-LOCATION:Asia/Jerusalem

BEGIN:STANDARD

DTSTART:20221030T010000

TZOFFSETFROM:+0300

TZOFFSETTO:+0200

TZNAME:IST

END:STANDARD

END:VTIMEZONE
END:VCALENDAR