Skip to main content

COVID-19 Resources

What people with cancer should know: https://www.cancer.gov/coronavirus

Guidance for cancer researchers: https://www.cancer.gov/coronavirus-researchers

Get the latest public health information from CDC: https://www.cdc.gov/coronavirus

Get the latest research information from NIH: https://www.covid19.nih.gov

Grant Details

Grant Number: 5R01CA151947-04 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: 2014


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.



Publications

A pair of datasets for microRNA expression profiling to examine the use of careful study design for assigning arrays to samples.
Authors: Qin L.X. , Huang H.C. , Villafania L. , Cavatore M. , Olvera N. , Levine D.A. .
Source: Scientific data, 2018-05-15; 5, p. 180084.
EPub date: 2018-05-15.
PMID: 29762551
Related Citations

Empirical evaluation of data normalization methods for molecular classification.
Authors: Huang H.C. , Qin L.X. .
Source: PeerJ, 2018; 6, p. e4584.
EPub date: 2018-04-11.
PMID: 29666754
Related Citations

Cautionary Note on Using Cross-Validation for Molecular Classification.
Authors: Qin L.X. , Huang H.C. , Begg C.B. .
Source: Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2016-11-10; 34(32), p. 3931-3938.
PMID: 27601553
Related Citations

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; 9(1), p. 27.
EPub date: 2016-06-10.
PMID: 27282150
Related Citations

Empirical insights into the stochasticity of small RNA sequencing.
Authors: Qin L.X. , Tuschl T. , Singer S. .
Source: Scientific reports, 2016-04-07; 6, p. 24061.
EPub date: 2016-04-07.
PMID: 27052356
Related Citations

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
Related Citations

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.
EPub date: 2015-12-13.
PMID: 26688660
Related Citations

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; 20(13), p. 3371-8.
EPub date: 2014-05-01.
PMID: 24788100
Related Citations

Finding gene clusters for a replicated time course study.
Authors: Qin L.X. , Breeden L. , Self S.G. .
Source: BMC research notes, 2014-01-24; 7, p. 60.
EPub date: 2014-01-24.
PMID: 24460656
Related Citations

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.
EPub date: 2014-10-15.
PMID: 25452684
Related Citations

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.
EPub date: 2014-06-06.
PMID: 24905456
Related Citations

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.
EPub date: 2015-09-03.
PMID: 26380547
Related Citations

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.
EPub date: 2013-03-18.
PMID: 23589668
Related Citations




Back to Top