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

Grant Number: 5R01CA151947-02 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: 2012
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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. PUBLIC HEALTH RELEVANCE: Microarrays are being widely used in cancer research. A critical step for processing microarray data is to normalize the arrays so that measurements from different arrays are comparable. There is a great need to evaluate the properties of statistical methods for array normalization when they are applied to microRNA arrays utilizing heterogeneous samples such as tumors.

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Publications

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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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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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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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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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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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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