Fig. 1

The flowchart of the classifier building process. a Data processing step. Fourteen datasets were collected and separated into training and validation datasets. Four hundred eleven most variable genes were then selected based on the median absolute deviation (MAD > 0.5), and were kept for the clustering process. b NMF clustering step. Six-cluster resulted the maximum cophenetic correlation coefficient was chosen as the optimal number of clusters. Then, NMF clustering were performed of 200 times with optimal number of clusters to obtain the consensus matrix. c Classifier building steps. A classifier was built on the most representative samples and most predictive genes for each cluster