FIG 5.

Prediction performance of the RF classifiers. Row indicates the RF classifier trained on the dataset-specific or common method-specific CRC markers; column indicates the classifier applying to the dataset of the corresponding column. In each three by three matrix of AUC values, diagonal values represent the AUC values of cross-validation obtained by using the trained row RF classifier on the column dataset, and off-diagonal values represent the AUC values of cross-cohort validation obtained by applying the trained row RF classifier on corresponding column dataset, (A) RF classifier was built from each dataset-specific markers (row). (B) RF classifier was built from the common markers present in at least two datasets from the USA, France, and China (common method-specific markers). ‘Average AUC score’ row represents the column average of the corresponding three-by-three AUC score matrix. Notation: e.g., DY_USA_sp_USA means classifier trained on the USA data based on the USA-specific markers identified by DyNet method, common_DY_USA means classifier trained on the USA data based on the common markers identified by DyNet method those are present in at least in two datasets DY, DyNet; LF, LEfSe; RF, random forest; and sp, specific.