Cell Type Deconvolution from Bulk RNA-seq Data with Probabilistic Machine Learning
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Author
Date
2023-08-21
Department
Major/Subject
Bioinformatics and Digital Health
Mcode
SCI3092
Degree programme
Master’s Programme in Life Science Technologies
Language
en
Pages
32 + 12
Series
Abstract
In gene expression studies, Bulk RNA-sequencing (bulk RNA-seq) is an attractive alternative to single-cell RNA-sequencing (scRNA-seq) when single-cell resolution is not required. However, the cell type composition of bulk RNA-seq samples is often unknown, which may lead to inaccuracies in the analysis. This thesis proposes DeconV, a probabilistic cell type deconvolution method that utilizes scRNA-seq as a reference to infer cell type proportions from bulk RNA-seq. The performance of DeconV is evaluated using three datasets and compared against three popular state-of-the-art methods from the literature, namely CIBERSORTx [1], MuSiC [2], and Scaden [3]. Furthermore, the impact of technical factors, such as the number of genes and gene expression normalization, on the deconvolution results is assessed. DeconV achieves comparable accuracy to the best performing method (Scaden) while improving the interpretability of the model and results.Description
Supervisor
Lähdesmäki, HarriThesis advisor
Meistermann, DimitriKilpinen, Helena
Keywords
deconvolution, gene expression, cell type, probabilistic modelling, single-cell, RNA-sequencing,
