Fig. 2: Benchmarking results of scDEAL.

a Optimized benchmarking results of all six datasets using scDEAL. Source data are provided as Source Data 1: optimized benchmarking results of seven metrics. b F1-score comparison using GDSC database only, CCLE database only, and both databases in training scDEAL for all six datasets. The bar plot shows the mean F1-scores of each data (n = 50; same parameter settings for each data; different seeds), with error bars representing +/− standard deviations. The same rules are also applied for the bar plots in c and d. Source data are provided as Source Data 2: F1-score of 50 repeated experiments comparing with and without transfer learning in six datasets. c Drug response prediction comparisons of scDEAL framework using common autoencoder (dark grey), denoising autoencoder (light grey), and the combination of denoising autoencoder in feature extraction and cell-type regularization in DaNN loss function for transfer learning (pink). Source data are provided as Source Data 3: F1-score of 50 repeated experiments comparing use GDSC, use CCLE, and use both bulk databases in six datasets. d Comparisons of scDEAL with (grey) and without (pink) transfer learning in terms of F1-scores. Source data are provided as Source Data 4: F1-score of 50 repeated experiments comparing use autoencoder, denoise autoencoder, and combination of denoise autoencoder and cell type regularization in six datasets. e Latent representations of scDEAL obtained with/without cell type regularization for Data 5 and 6. f Robustness test on six scRNA-seq datasets via 80% stratified sampling in terms of F1-score. Each box shows the minimum, first quartile, median, third quartile, and maximum F1-scores of 20 samplings (n = 20). Dots represent outliers. Source data are provided as Source Data 6: F1-scoreof 80% stratified sampling of 20 repeats on six datasets. Abbreviations: Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), denoising autoencoder (DAE).