Before running CoGA, you must set the execution parameters available on the left sidebar. Below, we detail each differential network analysis parameter.

Classes (conditions) being compared

CoGA analyses compare only two phenotypes. If your dataset has more than two phenotypes, then you must select one pair of phenotypes from a list of all possible pairs.

The selected pair will be used by all CoGA analyses.

Gene sets size range

CoGA performs tests for each gene set of a collection of gene sets. To test only a subcollection of sets, you can filter the gene groups according to their sizes by setting the "Minimum gene set size" and "Maximum gene set size" parameters.

The minimum gene set size allowed is 2. However, we recommend to test groups with at least 20 genes.

Testing large gene sets can spend much time. In general, it is feasible to set 1000 or some hundreds of genes as the maximum gene set size. However this number may vary according to the user's machine specification.

Method for network inference

The network links are inferred according to a measure of association between the gene expression levels. CoGA provides three classical association measures:

The correlation coefficient or p-value obtained by one of the methods mentioned above are used to set an association degree for each link of the network. The following options are available to measure the association degrees:

Network type

You can choose between unweighted and weighted networks:

Method for gene networks comparison

CoGA compares the gene co-expression networks between two phenotypes for each gene set.

Below, we describe the methods available for comparing unweighted networks:

CoGA includes generalizations of some of the statistics described above to weighted undirected graphs. Let G be a weighted undirected graph. We define the weighted adjacency matrix of G to be the matrix W = (w)ij, such that wij is the weight of the edge that connects the vertices vi and vj. In this context, 0 ≤ wij ≤ 1 and G is a full graph.

Below, we describe the methods available for comparing weighted networks:

For the "Spectral distribution test", the "Spectral entropy test", and the "Degree distribution test" methods, you must select a criterion to define the bandwidth for the probability density function estimation. The available methods for computing the bandwidth are:

CoGA uses the R 'density' function from the base package for estimating the probability density function.

Permutation test settings

To compute a p-value for the differential network analysis, CoGA performs a permutation based test, which generates N random permutations of the sample labels.

The minimum possible p-value is 1N + 1. Therefore, the choice of N depends on the required significance level of the test. You can set the N parameter on the "Enter the number of label permutations" option.

To perform the same label permutations for all gene sets, you can set a seed to generate the random permutations on the "Enter a seed to generate random permutations" option.

Running the analysis

After loading the dataset and the execution parameters, click on the "Start analysis" button. A progress bar will be shown on the right top corner of the page:

The results and other execution messages are shown on the "Analysis results" section.