2010
DOI: 10.1186/1471-2105-11-510
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Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets

Abstract: BackgroundMicroarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying … Show more

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Cited by 7 publications
(9 citation statements)
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“…Normalized spot volumes, i.e., the volume of each spot over the volume of all spots in the gel, were used for comparison of the different groups, and candidates were identified as protein spots that changed in expression level by Ͼ2-fold as compared with normal controls. Statistical significance was assessed by the analysis of variance test, and p values of Ͻ0.05 were considered significant for comparison (26).…”
Section: Methodsmentioning
confidence: 99%
“…Normalized spot volumes, i.e., the volume of each spot over the volume of all spots in the gel, were used for comparison of the different groups, and candidates were identified as protein spots that changed in expression level by Ͼ2-fold as compared with normal controls. Statistical significance was assessed by the analysis of variance test, and p values of Ͻ0.05 were considered significant for comparison (26).…”
Section: Methodsmentioning
confidence: 99%
“…the volume of each spot over the volume of all spots in the gel, were used for comparison of the different groups, and candidates were identified as protein spots that changed at least twofold versus their specific control. Statistical significance was assessed by a two-tailed Student's t test and analysis of variance test (ANOVA) (28), and p values of Ͻ0.05 were considered significant for comparison between control and experimental data.…”
Section: Methodsmentioning
confidence: 99%
“…Furthermore, the typically low numbers of replicates, given the cost of the technology, affects variance estimations. To circumvent the statistical limitations associated with the size of the datasets, the strictness of the threshold used to filter significant genes is increased as the number of replicates decreases [ 17 ]. However, a stricter threshold (type I error) decreases the power of the test (type II error), thereby increasing the rate of false negatives and limiting the detection of DEGs to those that are most obvious.…”
Section: Introductionmentioning
confidence: 99%