Logo: to the web site of Uppsala University

uu.sePublikasjoner fra Uppsala universitet
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
A benchmarking study of the Ising model fitted to single cell RNA-sequencing data for the estimation of differentiation potential
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för biologisk grundutbildning.
2023 (engelsk)Independent thesis Advanced level (professional degree), 20 poäng / 30 hpOppgave
Abstract [en]

Glioblastoma (GBM) is the most aggressive form of adult diffuse gliomas, and the most prevalent among malignant primary brain tumors. The prognosis is poor, with a median survival of 15 months and unavoidable tumor recurrence. It is believed that a small population of cells, called cancer stem cells (CSCs), reside within the tumor and are at least partly responsible for the tumor re-initiation. In this thesis, we evaluate a new computational measure, based on the Ising model from statistical physics, for identifying the most stemlike cells in a population by considering single cell RNA-sequencing data. We evaluate a range of different optimizations on the model, and conclude that the model is already optimized with respect to the considered refinements. We also compare it to two other computational stemness measures and conclude that the Ising model shows comparable performance. With further refinements, it will be a valuable computational measure for scoring the stemness of single cells, for simulating data from a population of cells, and for investigating the effect of a drug treatment or gene knockout. This will help the understanding of complex glioblastoma tumor tissue and will allow the exploration of possible treatments. 

sted, utgiver, år, opplag, sider
2023. , s. 50
Serie
UPTEC X ; 23024
Emneord [en]
differentiation potential, Ising models, single cell RNA sequencing, cancer stem cells, glioblastoma, entropy
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-506302OAI: oai:DiVA.org:uu-506302DiVA, id: diva2:1775307
Utdanningsprogram
Molecular Biotechnology Engineering Programme
Veileder
Examiner
Tilgjengelig fra: 2023-06-28 Laget: 2023-06-26 Sist oppdatert: 2023-06-28bibliografisk kontrollert

Open Access i DiVA

Fulltekst tilgjengelig fra 2026-06-25 10:00
Tilgjengelig fra 2026-06-25 10:00

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric

urn-nbn
Totalt: 351 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf