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SUMMARY:Single cells\, population dynamics\, and Euler characteristic prof
 iles
DTSTART:20260402T100000
DTEND:20260402T110000
DTSTAMP:20260503T091127Z
UID:e16d4ad47b25b3a1ea030a097f9dcfc54b3dcdd74f2ab9c77da45194
CATEGORIES:Conferences - Seminars
DESCRIPTION:Michael Bleher\, Universität Heidelberg University\nThe Eule
 r characteristic profile (ECP) of a multifiltered simplicial complex recor
 ds the Euler characteristic at each point in the filtration poset. While c
 ruder than multiparameter persistent homology\, ECPs are computationally m
 uch more tractable and often still sensitive enough to detect changes in t
 he topology of the underlying data. For example\, ECPs based on vector fie
 ld data are able to differentiate between dynamical systems in 2 and 3 dim
 ensions. In this talk\, I present a recent project for similarly extractin
 g dynamical information from high-dimensional point cloud data equipped wi
 th a vector field. The motivating application is single-cell RNA sequence 
 data\, where RNA velocity provides a proxy for the direction and rate of c
 ellular state transitions. We construct multifiltered flag complexes where
  edge weights are derived from distances and velocities. On synthetic data
  generated by a stochastic model of gene expression dynamics with known gr
 ound-truth transition graphs\, the resulting ECPs distinguish between comp
 eting state transition networks. Ultimately we want to use these ideas to 
 investigate neural stem cell differentiation -- both in homeostasis and wh
 en it goes wrong\, as in glioblastoma. This is joint work with Marta Marsz
 ewska\, Justyna Signerska-Rynkowska\, Paweł Dłotko\, Anna Marciniak-Czoc
 hra\, and Ana Martín-Vilalba.\n\n 
LOCATION:MA B1 504 https://plan.epfl.ch/?room==MA%20B1%20504
STATUS:CONFIRMED
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