Prediction of Cell Counts from DNA Methylation

dc.contributor.advisorSalumets, Ahto, juhendaja
dc.contributor.authorPähk, Simo
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Arvutiteaduse instituutet
dc.date.accessioned2023-08-18T12:30:42Z
dc.date.available2023-08-18T12:30:42Z
dc.date.issued2022
dc.description.abstractDNA methylation is an epigenetic factor that modulates gene expression. The close relationship between gene expression and cell differentiation serves as a basis for methylationbased cell mixture deconvolution—a method for determining the proportions of constituent cell types in a biological sample. Previous work has demonstrated its usefulness in predicting lymphocyte subtypes in blood samples, but has neglected TEMRA, a type of senescent lymphocyte associated with aging and autoimmune diseases. This thesis sets out to explore the feasibility of estimating the proportions of T cells in various stages of differentiation, including TEMRA, from methylation sequencing data using machine learning. The results show that while prediction accuracy is lower for TEMRA subtypes than for general subtypes such as T cells, it is nonetheless a viable approach for this task, especially since DNA sequencing is cheaper and more scalable than traditional laboratory methods for blood sample analysis.et
dc.identifier.urihttps://hdl.handle.net/10062/91660
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMethylationet
dc.subjectcell mixture deconvolutionet
dc.subjectTEMRAet
dc.subjectmachine learninget
dc.subjectregressionet
dc.subject.otherbakalaureusetöödet
dc.subject.otherinformaatikaet
dc.subject.otherinfotehnoloogiaet
dc.subject.otherinformaticset
dc.subject.otherinfotechnologyet
dc.titlePrediction of Cell Counts from DNA Methylationet
dc.typeThesiset

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Pahk_BSc_thesis_2022.pdf
Size:
2.38 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: