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| ====== Serial Expression Analysis ====== | ====== Serial Expression Analysis ====== | ||
| - | Serial Expression Analysis (SEA) is a web site for the analysis of serial gene expression data. Serial data is understood as multifactorial experimental designs where one of the factors is a quantitative variable such as time or treatment dose. The site offers five different methodologies for the identification of genes and functional classes which significant changes across series. | + | **S**erial **E**xpression **A**nalysis (**SEA**) is a web site for the analysis of serial gene expression data. Serial data is understood as multifactorial experimental designs where one of the factors is a quantitative variable such as time or treatment dose. The site offers five different methodologies for the identification of genes and functional classes which significant changes across series. \\ |
| - | \\ | + | The following table summarizes the main characteristics of the SEA algorithms:\\ |
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| + | |**Name**|**Statistical Strategy**|**Selected Features** |**Selection criterion**| | ||
| + | |maSigPro|Univariate Regression|Genes| Genes with differential expression profiles| | ||
| + | |maSigFun| Univariate Regression| Functional Categories|Functional classes with most genes having correlated differential expression profiles| | ||
| + | |ASCA-genes|ANOVA + Multivariate Projection|Genes|Genes that follow major expression trends| | ||
| + | |ASCA-functional|ANOVA + Multivariate Projection + GSA|Functional Categories|Functional classes associated to a given expression trend| | ||
| + | |PCA-maSigFun|Multivariate Projection + Univariate Regression|Functional Categories| Functional classes with subset of genes showing correlated differential expression profiles| | ||
