Bimodal genomic approach predicting Semaphorin 7A (SEMA7A) as prognostic biomarker in adrenocortical carcinoma

Bimodal Gene Detection using Gaussian Mixture Modeling in Tumor Expression Data. Description: This project provides an R-based analytical pipeline designed to identify genes with bimodal expression patterns across tumor samples. Bimodal expression may indicate tumor heterogeneity, subtype-specific gene regulation, or clinically relevant expression shifts. The pipeline uses: Gaussian Mixture Modeling (GMM) to fit two-component distributions for each gene Hartigan’s Dip Test to test for non-unimodality Required R Packages: readxl tidyverse diptest nor1mix You can install the required packages in R using: install.packages(c("readxl", "tidyverse", "diptest", "nor1mix")) How to Run: Ensure the expression matrix file named Gene_Expression.csv is placed in the same directory as the script. Then run: Rscript code.R This will generate the ranked list and plots for downstream analysis or interpretation.