Improving the statistical detection of regulated genes from microarray data using intensity-based variance estimation |
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| Authors: | Jason Comander Sripriya Natarajan Michael A Gimbrone Jr Guillermo García-Cardeña |
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| Institution: | (1) Center for Excellence in Vascular Biology, Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA;(2) Department of Pathology, Harvard Medical School, Boston, MA 02115, USA;(3) Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA 02139, USA |
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| 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. |
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