After performing the differential network analysis, you can explore several gene set properties on the "Further analysis" tab.

Below, we explain the available tools for exploring the gene sets.

Gene set selection

To analyze a gene set, select it from the list of gene sets on the "Further analysis" tab. The following options are available for filtering the lists of gene sets:

After selecting a gene set, choose a tab on the "Further analysis" section for analyzing the selected set.

Network visualization plots

Given a gene set name, the "Network visualization plots" tab on the "Further analysis" section provides tools to visually inspect the networks and the differences between the two phenotypes.

Below, we describe the available tools.

Plot settings
Network visualization

The network visualization tool plots one association matrix for each phenotype.

The association matrix contains the association degree of each pair of genes from the selected gene set. The associations degrees are measured from the expression data using the method selected on the left sidebar ("Method for network inference"). The degrees vary between 0 and 1, and are represented by colors. If the "absolute correlation" option is selected on the sidebar, the user can visualize both absolute and non absolute correlations by setting the "Show negative correlations" option. To save the plots, click on the "Save class 1 network plot" and "Save class 2 network plot" buttons.

You can check out the value of each association degree on the "Association between two gene products" section.

Differences between the gene networks

To visualize the differences between the networks, you can plot a matrix of the differences. Each position of the matrix shows the difference in the correlation (association degree) of two gene products between the two phenotypes.

You can choose one of the following options:

To plot the selected matrix of differences, just expand the "Matrix of differences" collapsing panel. You can save the plot by clicking on the "Save plot button".

To see the differences in association degree of each pair of genes, expand the "List of gene association degrees" collapsing panel. You can save the list as a CSV or R data file:

Gene set properties

To check out network features of each phenotype, select one of the options described below.

Network features for unweighted graphs:
Network features for weighted graphs:
Gene scores

To rank the genes according to their ``importance'' in the networks, select one of the options below.

Gene scores for unweighted graphs:
Gene scores for weighted graphs:

You can save the gene scores as a CSV or R data file:

Gene expression analysis

Below, we describe the available single gene differential analysis tools.

Gene expression heatmap

The gene expression heatmap represents the gene expression levels by colors. Each column corresponds to a gene of the selected gene set, and each row represents one sample.

You can select the colors of the heatmap, and set its clustering options. The rows or columns of the heatmap will be clustered according to the enclidean distance. You can set the heatmap dimensions and save it as a PDF, PNG or JPG file.

CoGA uses the pheatmap CRAN package to plot the heatmaps.

Tests for differential expression

CoGA tests the difference in average or median expression levels of each single gene of the selected gene set. Those analyses use the "t.test" and "wilcox.test" R functions.

The figure below shows the table of results:

Those results can be saved as a CSV or R data file:

Gene expression boxplot

To visualize the distribution of the gene expression levels in each phenotype, just select a gene from the list on the "Gene expression boxplot" section.

You can set the plot dimensions and save it as a PDF, PNG or JPG file.

CoGA boxplots are built with the ggplot2 package.