![]() ![]() To run code for sample data follow below steps :ġ- Create a new directory in you local system for example : TestĢ- Place Mergefile_top20.csv and MoCSOM.py files in that folderģ- Make sure python3 is installed and directory classpaths are set accordingly If the format is changed then the pre-processing file need to be adjusted accordingly. The pre-processing file is created to process the data of formats mentioned in gene_expression.csv, cna_norm.csv and dna_methylation.csv. Note: To include more than three omic data the code needs modification. This file is used as input for step 2 and so on. This sample file for 20 genes is attached which is called Mergefile_top20.csv. These files are gene_expression.csv, cna_norm.csv and dna_methylation.csv.Īfter applying Step 1 a merged file with all three omics of the shortlisted genes is generated. ![]() Input Files: This algorithm considers only 3 types of Omic data, Initially the data from cbioPortal is process and formatted into 3 files. Step 5 (5_Apply_cnn-metrics.py)- Use this file to run CNN algorithm on the images generated in the previous step replace the path of reflect the actual paths. Step 4 (4_MoveFilesRandomly.py)- This file is optional this is used to randomly divide the images into test and training set At the end of this script you have an image for each patient. replace the path variable "pathGE" with the file generated at the first step. Step 3 (3_CreateRGBImages.py)- replace the mappings or cordinates obtained in previous step with "pos" variable in this script. i.e., (x,y) coordinates for each gene make a note of this to use in next step. As a result of this script we get coordinates or mapping of genes. replace the path of the file at "pathGE" variable. Step 2 (2_CreateTemplate.py)- Once the data is pre-processed proceed with the creation of template file we conside only gene expression data for this step. As a result of the preprocessing script the result will be a merge file which has all three omic data for a set of patients/samples Replace the patient id's with patient_list and gene_list. ![]() To use this template create three different files of each type of omic data for a set of samples(Patient id) and a set of common genes. Step 1 (1_PreProcessing.py)- Process the data using pre-processing template provided. To replicate the algorithm use the following steps: An Integrative Approach for Transforming Multi-omic Data into Gene Similarity Networks via Self-organizing Maps ![]()
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