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National Institutes of Health: National Cancer Institute: Division of Cancer Control and Population Sciences
Grant Details

Grant Number: 5R01CA151947-03 Interpret this number
Primary Investigator: Qin, Li-Xuan
Organization: Sloan-Kettering Inst Can Research
Project Title: Statistical Methods for Normalizing Microarrays in Cancer Biomarker Studies
Fiscal Year: 2013
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Abstract

DESCRIPTION (provided by applicant): Various array normalization methods have been developed for gene expression microarrays. Most of these methods assume few or symmetric differential expression between sample groups. There has been no systematic study of the properties of these methods in normalizing microRNA expression arrays utilizing heterogeneous samples such as tumors. MicroRNA arrays contain only a few hundred microRNAs, and are likely to have a relatively large proportion being differentially expressed between diverse tumor groups. The assessment of normalization methods in this setting is difficult because of the lack of a benchmark dataset that has no confounding array effects. We propose to design and generate such benchmark datasets, perform a systematic assessment of normalization methods with a particular emphasis on the utility of these models for detecting markers with differential expression, and from the benchmark data design derive statistical models that acknowledge heterogeneities inherent to tumor samples.

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Publications

Study Design And Data Analysis Considerations For The Discovery Of Prognostic Molecular Biomarkers: A case Study of progression Free Survival In Advanced Serous Ovarian Cancer
Authors: Qin L.X. , Levine D.A. .
Source: Bmc Medical Genomics, 2016-06-10 00:00:00.0; 9(1), p. 27.
PMID: 27282150
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Empirical Insights Into The Stochasticity Of Small Rna Sequencing
Authors: Qin L.X. , Tuschl T. , Singer S. .
Source: Scientific Reports, 2016; 6, p. 24061.
PMID: 27052356
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Molecular Subtypes Of Uterine Leiomyosarcoma And Correlation With Clinical Outcome
Authors: Barlin J.N. , Zhou Q.C. , Leitao M.M. , Bisogna M. , Olvera N. , Shih K.K. , Jacobsen A. , Schultz N. , Tap W.D. , Hensley M.L. , et al. .
Source: Neoplasia (new York, N.y.), 2015 Feb; 17(2), p. 183-9.
PMID: 25748237
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Differential Expression Analysis For Rna-seq: An Overview Of Statistical Methods And Computational Software
Authors: Huang H.C. , Niu Y. , Qin L.X. .
Source: Cancer Informatics, 2015; 14(Suppl 1), p. 57-67.
PMID: 26688660
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Blocking And Randomization To Improve Molecular Biomarker Discovery
Authors: Qin L.X. , Zhou Q. , Bogomolniy F. , Villafania L. , Olvera N. , Cavatore M. , Satagopan J.M. , Begg C.B. , Levine D.A. .
Source: Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research, 2014-07-01 00:00:00.0; 20(13), p. 3371-8.
PMID: 24788100
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Finding Gene Clusters For A Replicated Time Course Study
Authors: Qin L.X. , Breeden L. , Self S.G. .
Source: Bmc Research Notes, 2014; 7, p. 60.
PMID: 24460656
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Microrna Array Normalization: An Evaluation Using A Randomized Dataset As The Benchmark
Authors: Qin L.X. , Zhou Q. .
Source: Plos One, 2014; 9(6), p. e98879.
PMID: 24905456
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Corm: An R Package Implementing The Clustering Of Regression Models Method For Gene Clustering
Authors: Shi J. , Qin L.X. .
Source: Cancer Informatics, 2014; 13(Suppl 4), p. 11-3.
PMID: 25452684
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Preprocessing Steps For Agilent Microrna Arrays: Does The Order Matter?
Authors: Qin L.X. , Huang H.C. , Zhou Q. .
Source: Cancer Informatics, 2014; 13(Suppl 4), p. 105-9.
PMID: 26380547
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An Empirical Evaluation Of Normalization Methods For Microrna Arrays In A Liposarcoma Study
Authors: Qin L.X. , Tuschl T. , Singer S. .
Source: Cancer Informatics, 2013; 12, p. 83-101.
PMID: 23589668
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