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A Critical Review of Gene Marker Selection Methods and Cell Count Inference Tools

Wang, Muying (2017) A Critical Review of Gene Marker Selection Methods and Cell Count Inference Tools. Master's Thesis, University of Pittsburgh. (Unpublished)

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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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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Wang, Muyingmuw2@pitt.edumuw2
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairShoemaker, Jason E.jason.shoemaker@pitt.edujas518
Committee MemberBanerjee, Ipsitaipb1@pitt.eduipb1
Committee MemberParker, Robert
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

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