Random forest analysis is a machine learning method widely used in classification and regression problems. Using the method of random forest, evaluate the contribution of features to classification, mine key driving factors such as marker species, metabolites, or genes, and select the features that contribute the highest accuracy to sample grouping prediction.

1.Input

1.1 Sample Grouping and Abundance Table

A tab separated text file containing row and column names, with each row representing a sample, the first column representing the sample name, Second column: Grouping information (column header fixed as "Category")and the remaining columns representing a gene.

2.Output

3.Only the 15 features with the highest contribution are shown in the figure, and the closer the point corresponding to each feature is to the right, the greater its contribution to the accuracy of group prediction. The color bar on the right represents expression levels of the feature across experimental groups (Control vs. Treatment), with red indicating high expression and green denoting low expression.