BEGIN:VCALENDAR
VERSION:2.0
PRODID:researchseminars.org
CALSCALE:GREGORIAN
X-WR-CALNAME:researchseminars.org
BEGIN:VEVENT
SUMMARY:Christophe Biscio (Aalborg University)
DTSTART;VALUE=DATE-TIME:20220929T131500Z
DTEND;VALUE=DATE-TIME:20220929T140000Z
DTSTAMP;VALUE=DATE-TIME:20220927T042605Z
UID:gbgstats/1
DESCRIPTION:Title: Asymptotic topological data analysis for point processes\nby Christop
he Biscio (Aalborg University) as part of Gothenburg statistics seminar\n\
nLecture held in MVL14.\n\nAbstract\nTopological Data Analysis has in the
past year attracted more attention in various fields such as in material s
ciences to study the properties of porous material or in statistics to stu
dy the asymptotic properties of random objects. However\, topological data
analysis still appears hard to grasp for many statisticians. \n\nThis tal
k intends to be an introduction to topological data analysis and therefore
does not require any background in the field. We will present an overview
of the different approaches in topological data analysis and will focus o
n the persistent homology approach. \nWe will present the framework of thi
s approach and its main mathematical objects. \nFinally\, we come back to
the land of Probability and will present a central limit theorem for the s
o-called Betti numbers obtained from stationary point processes\, non-nece
ssarily Poisson.\n
LOCATION:https://researchseminars.org/talk/gbgstats/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Anders StÃ¥hlberg & Serik Sagitov (Chalmers & University of Gothen
burg)
DTSTART;VALUE=DATE-TIME:20221006T131500Z
DTEND;VALUE=DATE-TIME:20221006T140000Z
DTSTAMP;VALUE=DATE-TIME:20220927T042605Z
UID:gbgstats/2
DESCRIPTION:Title: Counting molecular identifiers in sequencing using a multitype branching
process with immigration\nby Anders StÃ¥hlberg & Serik Sagitov (Chalme
rs & University of Gothenburg) as part of Gothenburg statistics seminar\n\
nLecture held in MVL14.\n\nAbstract\nDetection of extremely rare variant a
lleles\, such as tumour DNA\, within a complex mixture of DNA molecules is
experimentally challenging due to sequencing errors. Barcoding of target
DNA molecules in library construction for next-generation sequencing provi
des a way to identify and bioinformatically remove polymerase induced erro
rs. During the barcoding procedure involving $t$ consecutive PCR cycles\,
the DNA molecules become barcoded by unique molecular identifiers (UMI). D
ifferent library construction protocols utilise different values of $t$. T
he effect of a larger $t$ and imperfect PCR amplifications is poorly descr
ibed. \n\nThis paper proposes a branching process with growing immigration
as a model describing the random outcome of $t$ cycles of PCR barcoding
. Our model discriminates between five different amplification rates $r_1$
\, $r_2$\, $r_3$\, $r_4$\, $r$ for different types of molecules associated
with the PCR barcoding procedure. We study this model by focussing on $C_
t$\, the number of clusters of molecules sharing the same \nUMI\, as well
as $C_t(m)$\, the number of UMI clusters of size $m$. Our main finding i
s a remarkable asymptotic pattern valid for moderately large $t$. It turns
out that \n$E(C_t(m))/E(C_t)\\approx 2^{-m}$ for $m=1\,2\,\\ldots$\, rega
rdless of the underlying parameters $(r_1\,r_2\,r_3\,r_4\,r)$. The knowled
ge of the quantities $C_t$ and $C_t(m)$ as functions of the experimental p
arameters $t$ and $(r_1\,r_2\,r_3\,r_4\,r)$ will help the users to draw mo
re adequate conclusions from the outcomes of different sequencing protocol
s.\n
LOCATION:https://researchseminars.org/talk/gbgstats/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Peter Guttorp (University of Washington/Norwegian computing center
)
DTSTART;VALUE=DATE-TIME:20221027T131500Z
DTEND;VALUE=DATE-TIME:20221027T140000Z
DTSTAMP;VALUE=DATE-TIME:20220927T042605Z
UID:gbgstats/3
DESCRIPTION:Title: Comparing recent climate models to data\nby Peter Guttorp (University
of Washington/Norwegian computing center) as part of Gothenburg statistic
s seminar\n\nLecture held in MVL14.\n\nAbstract\nThe latest climate model
intercomparison project (CMIP6) was the basis for the sixth assessment rep
ort of the Intergovernmental Panel on Climate Change. The design of CMIP6
included climate runs with historical forcings\, meant to be comparable to
observational data. We will focus on global annual mean temperature\, a c
ommon (but not particularly sensitive) measure of climate change. Using fo
ur observational products provided with uncertainty assessments\, we combi
ne these into a single series. In doing so\, we estimate a smooth trend an
d a residual spectral density function\, with attendant simultaneous confi
dence bands. Using the same kind of decomposition of 318 climate model run
s from 58 models in the historical CMIP6 experiment\, we see how well the
model runs agree with the data. We also compare the warming between 1880-1
899 and 1995-2014. This is joint work with Peter Craigmile of the Ohio Sta
te University.\n
LOCATION:https://researchseminars.org/talk/gbgstats/3/
END:VEVENT
END:VCALENDAR