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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA151947-02
Application #
8231280
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Dunn, Michelle C
Project Start
2011-03-01
Project End
2015-02-28
Budget Start
2012-04-03
Budget End
2013-02-28
Support Year
2
Fiscal Year
2012
Total Cost
$405,398
Indirect Cost
$183,748
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065
Qin, Li-Xuan; Zhou, Qin (2014) MicroRNA array normalization: an evaluation using a randomized dataset as the benchmark. PLoS One 9:e98879
Qin, Li-Xuan; Breeden, Linda; Self, Steven G (2014) Finding gene clusters for a replicated time course study. BMC Res Notes 7:60
Qin, Li-Xuan; Zhou, Qin; Bogomolniy, Faina et al. (2014) Blocking and randomization to improve molecular biomarker discovery. Clin Cancer Res 20:3371-8
Qin, Li-Xuan; Tuschl, Tom; Singer, Samuel (2013) An Empirical Evaluation of Normalization Methods for MicroRNA Arrays in a Liposarcoma Study. Cancer Inform 12:83-101