Figure 1.

Identification of Parkinson-Glioma feature genes. GSE7621: 9 normal brain samples and 16 PD brain samples GSE20141: 8 normal brain samples and 10 PD brain samples GSE49036: 8 normal brain samples and 20 PD brain samples. (A) The boxplot illustrated the distribution of gene expression before the removal of batch effects; (B) Principal Component Analysis (PCA) demonstrated expression patterns before the elimination of batch effects; (C) The boxplot depicted the distribution of gene expression after batch effect removal; (D) PCA displayed expression patterns following batch effect removal; (E) Box plots of sample residuals from the eight algorithms were presented. The x-axis represented the quantile of outliers, with the red dot indicating the mean; (F) Eight different algorithms identified the top 10 significant genes, resulting in the discovery of 30 Parkinson-Glioma feature genes; (G) Reverse Cumulative Distribution Maps of model residuals were constructed using RF, SVM, XGB, GLM, Elastic Net, stepLDA, PLS, and msaenet. The y-axis represented the outlier percentile.