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Expression documentation

Differential expression:

Babelomics set of tools for differential gene expression analysis can be found in the Differential expression button of the Tools drop down menu. There are four different experimental contexts that you can explore using Babelomics:

Class comparison

The methods implemented here allow you finding genes differentially expressed within a class, between two or more than two classes. You can find the genes that have expression patterns more differentiated among them.

Methods - Input form - Output results

Correlation

Using this module, you can study gene expression related to a continuous variable. For example, if you treat some cells with different doses of a drug and you also measure their gene expression levels, you can find genes which expression increases or decreases with the level of treatment. Study expression among more two or more array classes. The methods implemented here allow you finding genes with similar expression.

Methods - Input form - Output results

Survival

Exploring gene expression related to a survival time. You can study for example which genes are more directly related to the death of your cells by analyzing the relationships between the expression of the genes and the survival time of the cells. Study the relationships between the expression of the genes and the survival time of the cells.

Methods - Input form - Output results

Time/dosage series

The module finds genes with a changing pattern along time or increasing dose concentrations, and with different profile evolutions between different series (i.e. treatments, strains, tissues, etc).

Methods - Input form - Output results

Predictors

Class prediction

Builds prediction rules and allows using them for further sample classification.

Method - Input form

Clustering

These methods use (implicitly or explicitly) a distance function and an algorithm to join together genetic elements that are more similar among them.

Methods - Input form - Output results