Liang, Tianyuzhou
(2024)
Pan-Tissue Cellular Deconvolution Using Single-Cell RNA-Seq References.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Critical questions in biomedical research, such as disease mechanisms and biological processing, require an understanding of cell type proportions in heterogeneous tissues. Due to the complexity of measuring cellular fractions with traditional experimental methods, computational cellular deconvolution methods have been developed to estimate these fractions based on gene expression data. Previously, EnsDeconv, an R package that implements ensemble deconvolution by leveraging multiple deconvolution methods and scenarios, was developed and has been proven to provide a more accurate and robust method to deconvolve bulk gene expression data and estimate cellular fractions. To optimize the package's utility and create a comprehensive cellular deconvolution atlas for the entire human body, we aim to incorporate single-cell RNA sequencing (scRNA-seq) references to deconvolve bulk expression data spanning 43 tissue types into 192 distinct cell types.
Using the EnsDeconv package, cellular fractions of 43 Genotype-Tissue Expression (GTEx) bulk samples were estimated based on the corresponding references curated from multiple large-scale scRNA-seq atlases, spanning over 60 datasets and 1.5 million cells. The usage of the estimated cellular fractions was demonstrated with our identified interesting associations between cellular fractions and covariates.
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| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
14 May 2024 |
| Date Type: |
Publication |
| Defense Date: |
10 April 2024 |
| Approval Date: |
14 May 2024 |
| Submission Date: |
23 April 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
32 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Cellular fraction estimation |
| Date Deposited: |
14 May 2024 19:09 |
| Last Modified: |
14 May 2024 19:09 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/46257 |
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