Fig. 4From: DeepMF: deciphering the latent patterns in omics profiles with a deep learning methodDeepMF’s imputation and factorization effect on cancer data sets with 70% random dropout. a-d The heatmap presentation and Silhouette width of four cancer data sets with 70% random dropout. The gray tiles in heatmap indicate missing entries. From left to right: matrix with 70% random dropout, after mean impute, after SVDImpute, after DeepMF. a Medulloblastoma data set b Leukemia data set c TCGA BRCA data set d SRBCT data set e Clustering accuracy of cancer subtyping on sample latent matrices generated by two imputations and five matrix factorization tools on different cancer data sets with 70% random dropoutBack to article page