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BMCBI
2005
112views more  BMCBI 2005»
13 years 6 months ago
Visualization methods for statistical analysis of microarray clusters
Background: The most common method of identifying groups of functionally related genes in microarray data is to apply a clustering algorithm. However, it is impossible to determin...
Matthew A. Hibbs, Nathaniel C. Dirksen, Kai Li, Ol...
BMCBI
2004
106views more  BMCBI 2004»
13 years 5 months ago
Spotting effect in microarray experiments
Background: Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects...
Tristan Mary-Huard, Jean-Jacques Daudin, Sté...
BMCBI
2005
148views more  BMCBI 2005»
13 years 6 months ago
Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments
Background: Numerous nonparametric approaches have been proposed in literature to detect differential gene expression in the setting of two user-defined groups. However, there is ...
Xin Gao, Peter X. K. Song
BMCBI
2005
86views more  BMCBI 2005»
13 years 6 months ago
WebArray: an online platform for microarray data analysis
Background: Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematic...
Xiaoqin Xia, Michael McClelland, Yipeng Wang
BMCBI
2008
132views more  BMCBI 2008»
13 years 6 months ago
Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of
Background: Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analys...
Raffaele Giancarlo, Davide Scaturro, Filippo Utro