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ISSUES IN META-ANALYSIS OF CANCERMICROARRAY STUDIES: DATA DEPOSITORY INR AND A META-ANALYSIS METHOD FORMULTI-CLASS BIOMARKER DETECTION

LU, SHU-YA (2009) ISSUES IN META-ANALYSIS OF CANCERMICROARRAY STUDIES: DATA DEPOSITORY INR AND A META-ANALYSIS METHOD FORMULTI-CLASS BIOMARKER DETECTION. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Systematic information integration of multiple related microarray studies has become an important issue as the technology has become significant mature and more prevalent in public health relevance over the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multiclass studies and expression pattern concordance are rarely explored. We first consider a natural extension of combining p-values from the traditional ANOVA model. Since p-values from ANOVA do not guarantee to reflect the concordant expression pattern information across studies, we propose a multi-class correlation measure (MCC) to specifically look for biomarkers of concordant inter-class patterns across a pair of studies. For both approaches, we focus on identifying biomarkers differentially expressed in all studies (i.e. ANOVA-maxP and min-MCC). The min-MCC method is further extended to identify biomarkers differentially expressed in partial studies using an optimally-weighted technique (OW-min-MCC). All methods are evaluated by simulation studies and by three meta-analysis applications to multi-tissue mouse metabolism data sets, multi-condition mouse trauma data sets and multi-malignant-condition human prostate cancer data sets. The results show complementary strength of ANOVA-based and MCC-based approaches for different biological purposes. For detecting biomarkers with concordant inter-class patterns across studies, min-MCC has better power and performance. If biomarkers with discordant inter-class patterns across studies are expected and are of biological interests, ANOVA-maxP better serves this purpose.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
LU, SHU-YAlushuya@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairTseng, George Cctseng@pitt.eduCTSENG
Committee MemberChang, (Joyce) Chung-Chou Hchangjh@upmc.edu
Committee MemberTang, Gonggot1@pitt.eduGOT1
Committee MemberWeissfeld, Lisalweis@pitt.eduLWEIS
Date: 29 September 2009
Date Type: Completion
Defense Date: 15 July 2009
Approval Date: 29 September 2009
Submission Date: 27 July 2009
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: meta-analysis; microarray; R package
Other ID: http://etd.library.pitt.edu/ETD/available/etd-07272009-153919/, etd-07272009-153919
Date Deposited: 10 Nov 2011 19:54
Last Modified: 15 Nov 2016 13:47
URI: http://d-scholarship.pitt.edu/id/eprint/8662

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