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

Column classes name

After input, the data variables values data BioNetStat will choose the first character (factor) column to classify the samples. Suppose your dataset has more than one column which labels the sample classes. In that case, it is possible to select which column of classes you want to use.

Classes (conditions) being compared

Select the classes you want to analyze with BioNetStat.

Gene sets size range

BioNetStat performs tests for each variable set of a collection of sets defined in the Variable set database. If the user inputted no file, the program would analyze only one group with all variables. To test only a subcollection of sets, you can filter the groups according to their sizes (number of variables). It is possible to set the "Minimum gene set size" and "Maximum gene set size" parameters.

The minimum gene set size allowed is 5. However, we recommend testing groups with at least 15 variables.

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

Method for network construction

The network links are inferred according to a measure of association between the values of the variables. BioNetStat provides three classical association measures:

Network type

You can choose between unweighted and weighted networks:

Statistic to link formation

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:

After choosing the association measure, the user has to select the threshold value to links formation.

Links weights

If the user selected the weighted option, he also has to choose which measure will be used as the weight of the links (Section 2d: Set the criterion for network edges weights) ).

Method for gene networks comparison

BioNetStat compares the correlation networks between the classes for each variable set.

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

BioNetStat 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:

BioNetStat 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, BioNetStat 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 variable 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. The warning "The analysis is running..." will be shown on the "Analysis Results" section:

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

Note: If an error occurs during the analysis, the page gets grey, as in the example below. In this case, restart the analysis, reloading the application.