BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20241120T082410Z
LOCATION:HG F 30 Audi Max
DTSTART;TZID=Europe/Stockholm:20240603T190100
DTEND;TZID=Europe/Stockholm:20240603T190200
UID:submissions.pasc-conference.org_PASC24_sess156_posC101@linklings.com
SUMMARY:P12 / ACMP01 - DeCovarT, a Multidimensional Probalistic Model for 
 the Deconvolution of Heterogeneous Transcriptomic Samples
DESCRIPTION:Poster\n\nBastien Chassagnol (University of Paris VI, ardata);
  Grégory Nuel (University of Paris VI); and Etienne Becht (INSERM)\n\nAlth
 ough bulk transcriptomic analyses have greatly contributed to a better und
 erstanding of complex diseases, their sensibility is hampered by the highl
 y heterogeneous cellular compositions of biological samples. To address th
 is limitation, computational deconvolution methods have been designed to a
 utomatically estimate the frequencies of the cellular components that make
  up tissues, typically using reference samples of physically purified popu
 lations. However, they perform badly at differentiating closely related ce
 ll populations. We hypothesized that the integration of the covariance mat
 rices of the reference samples could improve the performance of deconvolut
 ion algorithms. We therefore developed a new tool, DeCovarT, that integrat
 es the structure of individual cellular transcriptomic network to reconstr
 uct the bulk profile. Specifically, we inferred the ratios of the mixture 
 components by a standard maximum likelihood estimation (MLE) method, using
  the Levenberg-Marquardt algorithm to recover the maximum from the paramet
 ric convolutional distribution of our model. We then considered a reparame
 trization of the log-likelihood to explicitly incorporate the simplex cons
 traint on the ratios. Preliminary numerical simulations suggest that this 
 new algorithm outperforms previously published methods, particularly when 
 individual cellular transcriptomic profiles strongly overlap.\n\nSession C
 hair: Erik W. Draeger (Lawrence Livermore National Laboratory)
END:VEVENT
END:VCALENDAR