Functions
The framework consists of 5 R functions and 2 Python scripts. The order and flow of files and data is displayed in the following flowchart.
The flowchart displays the data stream of the whole framework. The blue rectangles represent functions of the R-package, while the orange rectangles represent the python scripts. All displayed graphs are generated in those functions. The transfer format is mentioned in the arrows between the functions.
As entry into the framework, a JSON file in the presented format is necessary. With the functions readjsonsheet() or spike_in(), a table, that contains all properties for each modality is generated. This is given to the calcscorematrix() function which generates the first plots that display the statistical properties of the given data. The normalization process is also completed at this point.
The generated adjacency matrix is passed to the buildsinglenode() function, which reduces the number of vertices and filters the edges to obtain a compute-able amount of edges.
The list of these genes is given to the graph analysis and the results are handled and validated by the readcluster() function. It also generates new lists for each found cluster, that contains all edges and modalities, that have been filtered in the buildsinglenode() function. In the last step of the framework, those lists are given to a second graph analysis. This is done in order to gain a higher resolution of the clusters, which has an impact on the significance of the discovered structures due to the fact that non-specific members of clusters are sorted out.
A more precise explanation of parameters and functionality of each function is given here: