loosolab/parrot repository overview

⁠PAthway pRedictiOn by mulTimodal genes

⁠Abstract

The aim of this framework is to gather new insights into the regulation, connection and correlation between genes. It could be shown that the expression level of some genes have a significant impact in the survival prognoses of cancer patients. To determine if a patient belongs to the high or low expressing group a multi modal distribution has to be fitted on the patient distribution of a gene. Multimodal genes of a dataset can be identified with the help of multimodalR⁠. The needed input file (JSON) can also be produced with the help of this R-package.

On base of these information gene interactions are suggested.

The separate parts and a more precise documentation can be obtained from the GitHub⁠

⁠Input

As input all data can be used, which show a multimodal distribution. The package was first tested with mRNA data from TCGA. A standardized format was needed to allow the formation of a connection between the multimodality recognition and the pathway analyses. This JavaScript Object Notation (JSON) contains each gene with its number of modalities and the connected means, standard deviation, sizes and the belonging patients in the parameter “groups”. Also, the location of the files that contain clinical data and the expression matrix should be given. An example of the needed data format is given here:

{
           "Genes": {
                      "Ensemble gene name": {
                                 "modus": 2,
                                 "means": [0.7,3.1],
                                 "sds": [284, 752],
                                 "sizes": [284, 752],
                                 "groups": [[Patients],
                                                    [Patients]]
           }
           "ClincalData": "pathTo/clinicalData",
           "Expressionmatrix": "pathTo/expressionmatrix"
}

⁠Workflow of the container

As entry into the framework, a JSON file in the presented format is necessary. With the functions readjsonmatrix() 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 computable 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 description of the functions of the container can be obtained here⁠.

⁠How to use this docker container

To prepare the environment for this container two empty directories have to be created. Those are the INPUT and OUTPUT directory. By placing ONE JSON file of the described format in the INPUT directory the analyses can be started by using this command:

$ docker run -i -v  <your INPUT directory>:/INPUT -v <your OUTPUT directory>:/OUTPUT loosolab/parrot