NDLI: Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation

Content Provider	Springer Nature : BioMed Central
Author	Comander, Jason Natarajan, Sripriya Gimbrone, Michael A García-Cardeña, Guillermo
Abstract	Background Gene microarray technology provides the ability to study the regulation of thousands of genes simultaneously, but its potential is limited without an estimate of the statistical significance of the observed changes in gene expression. Due to the large number of genes being tested and the comparatively small number of array replicates (e.g., N = 3), standard statistical methods such as the Student's t-test fail to produce reliable results. Two other statistical approaches commonly used to improve significance estimates are a penalized t-test and a Z-test using intensity-dependent variance estimates. Results The performance of these approaches is compared using a dataset of 23 replicates, and a new implementation of the Z-test is introduced that pools together variance estimates of genes with similar minimum intensity. Significance estimates based on 3 replicate arrays are calculated using each statistical technique, and their accuracy is evaluated by comparing them to a reliable estimate based on the remaining 20 replicates. The reproducibility of each test statistic is evaluated by applying it to multiple, independent sets of 3 replicate arrays. Two implementations of a Z-test using intensity-dependent variance produce more reproducible results than two implementations of a penalized t-test. Furthermore, the minimum intensity-based Z-statistic demonstrates higher accuracy and higher or equal precision than all other statistical techniques tested. Conclusion An intensity-based variance estimation technique provides one simple, effective approach that can improve p-value estimates for differentially regulated genes derived from replicated microarray datasets. Implementations of the Z-test algorithms are available at http://vessels.bwh.harvard.edu/software/papers/bmcg2004 .
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/1471-2164-5-17.pdf
Ending Page	21
Page Count	21
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/1471-2164-5-17
Journal	BMC Genomics
Issue Number	1
Volume Number	5
Language	English
Publisher	BioMed Central
Publisher Date	2004-02-27
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Outlier Detection Image Processing Technique Pool Standard Deviation Measured Standard Deviation Agilent Feature Extraction Software
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

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