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Computational Deconvolution of Bulk Tissue Transcriptomic Data
Hashemi, Tahoura Sadat | 2022
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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 55002 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Motahari, Abolfazl
- Abstract:
- Bulk tissue RNA-seq data has been widely used for investigating the transcriptome and analyzing it for different purposes. A single bulk sample of a heterogeneous population includes different cell-types each in different proportions. Bulk tissue RNA-seq measures the average expression level of genes across these cell types and does not account for cross-subject variation in cell-type compositions. Furthermore, biological signals might be masked by taking the average of gene expressions. Because of these reasons, bulk-RNA-seq is not suffcient for studying complex tissues. Knowing these cell-type compositions are important, because studying cell-specific changes in the transcriptome might be needed. The development of scRNA-seq has allowed scientists to measure the distribution of expression levels for each gene across a population of cells. Since scRNA-seq provides expressional information of single cells, the problems regarding bulk RNA-seq could be overcome. Alas, scRNA- seq is still costly and therefore not applicable to studies with large number of subjects. Throughout the years, many different approaches have been introduced to identify and analyze celltype-specific changes in complex tissues, one of which is computational deconvolution. In this paper we assess the ability of a computational deconvolution method in properly deconvolving bulk expression data of planaria tissues using single-cell RNA-seq data and propose a Bayesian method to utilize multiple single cell RNA-seq references in the process of deconvolution of bulk RNA-seq data
- Keywords:
- Deconvolution ; Gene Expression Data ; RNA Sequencing ; Single Cell Sequencing ; Non-Negative Least Squares (NNLS) ; Bulk RNA-seq ; Single Cell RNA Sequencing (scRNA-seq) ; Computational Deconvolution