Wang, Muying
(2017)
A Critical Review of Gene Marker Selection Methods and Cell Count Inference Tools.
Master's Thesis, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Seasonal influenza virus is a threat for human being. Understanding dynamical change of immune response induced by influenza infection could benefit diagnosis and drug development, using transcriptome analysis. But transcriptomic data is often complicated by the changing cell makeup of the tissue during disease. It’s difficult to distinguish between gene regulations and cell proliferation or migration. Therefore inference of the change in cell counts is necessary, and computational models for cell count inference are introduced in this thesis. Besides, in most models related to prediction of cell quantities, gene marker selection is used as the first step. Thus computational methodology concerning gene marker selection for cell count inference is also reviewed.
Different gene marker selection methods are applied to a common dataset to evaluate their behaviors. The uniqueness and expression intensity are the key properties for evaluation of obtained markers. As for predicting cell enrichment, principles of three kinds of schemes are explained. Computational algorithms named CTen and CIBERSORT are introduced as examples of them. Estimation behaviors of these tools are tested by a microarray dataset. Analysis of the estimations shows that they may provide good estimation but are not suitable for careful study of complex problems, e.g. dynamical samples.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
13 June 2017 |
| Date Type: |
Publication |
| Defense Date: |
13 March 2017 |
| Approval Date: |
13 June 2017 |
| Submission Date: |
4 April 2017 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
77 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Chemical Engineering |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
cell count, immune cell, deconvolution,gene marker selection |
| Date Deposited: |
13 Jun 2017 14:53 |
| Last Modified: |
13 Jun 2017 14:53 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/31299 |
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