• limma: This option allows us to detect genes with a significant gene expression (different to 0) between several two-colors arrays in the same experimental condition or class.
  Limma is a package for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. This option estimates the variability of data using a diferent method. More information.

The purpose of this set of tests is to study differential expression within a group, between two groups or more than two classes of arrays.

• t-test: This option performs, for each gene, a t-test for the difference in mean expression between the two groups of arrays. T-statistics and p-values are reported.
  In the output file as well as in the image, genes are ranked according to the t-statistic. Genes in the top of the results list are those more expressed in your first class. Genes in the bottom part of the list are those more expressed in the second class. More information.

• limma: This method permorms a similar testInterpretation of limma results is like t-test results. Limma is a package for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. This option estimates the variability of data using a diferent method. More information.

• fold-change: Fold-change analysis is used to identify genes with expression ratios or differences between two classes that are outside of a given cutoff or threshold. If you normalized data includes logarithmic transformation, you should calculate fold-change as the difference between means of two classes. In another case, you can calculate fold-change as log2 of ratio between means of two classes.

The purpose of this set of tests is to study differential expression among more than two groups or classes of arrays. The methods implemented here allow you finding genes differentially expressed between more than two classes.

• ANOVA: For each gene, this option performs a classical analysis of variance to test for mean differences between the array groups defined by the class variable. More information.

• limma: Interpretation of limma results is like ANOVA results. Limma is a package for the analysis of gene expression microarray data, especially the use of linear models for analysing designed experiments and the assessment of differential expression. This option estimates the variability of data using a diferent method. More information.