Single-cell and bulk transcriptomics with machine learning decode B cell hub genes and diagnostic biomarkers in anal fistula

Figure 5 Machine learning-based identification of key genes related to anal fistula-associated B cells. A: Box plot showing the expression levels of 19 candidate genes in B cells from anal fistula (AF) and control samples. Four genes were significantly upregulated in the AF group; B: Support vector machine model performance: Accuracy peaked (0.71) and error rate reached a minimum (0.29) when 13 variables were used; C: Coefficient profiles from least absolute shrinkage and selection operator (LASSO) regression analysis of the 19 genes across a range of penalty values (logλ); D: LASSO cross-validation plot identifying three key genes; E: Variable importance ranking of the top 15 genes derived from random forest analysis; F: Boruta algorithm identified 6 important genes; G: Extreme gradient boosting model ranking of variable importance across all input genes; H: Venn diagram showing the intersection of genes identified by five machine learning algorithms, with three common genes emerging as core candidates. ^aP < 0.05; ^bP < 0.01; ^cP < 0.001. AF: Anal fistula; LASSO: Least absolute shrinkage and selection operator; RF: Random forest; XGBoost: Extreme gradient boosting; SVM: Support vector machine.