Skip to main content
Fig. 3 | BMC Bioinformatics

Fig. 3

From: Transforming L1000 profiles to RNA-seq-like profiles with deep learning

Fig. 3

Comparing similarity between predicted and real profiles at the 978-landmark space (A, B). Violin plots of sample-wise Pearson’s correlation coefficients (PCCs) (A) and RSME (B) between predicted and real RNA-seq profiles (blue); between predicted by a baseline model and real RNA-seq profiles (orange); input L1000 signatures and predicted RNA-seq-like profiles (green); input L1000 and real RNA-seq profiles (red); and between predicted RNA-seq profiles and randomly paired real RNA-seq profiles (purple). Comparing similarity between predicted and real profiles at the full genome space (C, D). Violin plots of sample-wise Pearson’s correlation coefficients (C) and RSMEs (D) between predicted and real RNA-seq profiles (blue), between predicted RNA-seq profiles and randomly paired real RNA-seq profiles (orange), and between real RNA-seq profiles and randomly paired real RNA-seq profiles (green). Comparing similarity between predicted and real profiles at the gene level in the full genome space (E, F). Violin plots of gene-wise PCCs (E) and RSMEs (F) between predicted and real RNA-seq profiles (blue), between predicted RNA-seq profiles and randomly paired real RNA-seq profiles (orange), and between real RNA-seq profiles and randomly paired real RNA-seq profiles (green)

Back to article page