Fig. 3
From: Deep learning enables the quantification of browning capacity of human adipose samples
Deep learning reveals browning degree and signature genes of human brown-like fats. A An autoencoder model for interpreting human adipose samples. B Heatmap showing the correlation between the autoencoder features of GO term “Thermogenesis”. C Correlation between the autoencoder feature 72 (F72) and GO term “Thermogenesis” (n = 663). D Overviews of the association between biological features and F72 (n = 663). Columns represent samples sorted by F72 levels from low to high. Rows represent molecular and biological processes associated with F72, including fatty acid (FA) biosynthesis, FA degradation, PPARα activity, PPARβ activity and PPARγ activity. E Correlation between F72 levels and z-scored ssGSEA scores of inflammation-related pathway activity (n = 663). F Relative abundance of indicated immune cell types in F72 high group and low group (n = 663). G Correlation between F72 levels and the expression levels of the signature genes (n = 663)