iHerd: an integrative hierarchical graph representation learning framework to quantify network changes and prioritize risk genes in disease
Fig 4
iHerd identifies different divergent genes between cell types.
(a) Different neuronal and non-neuronal groups in UMAP using RNA. Here are seven samples and 84,852 cells. (b) The dot plot colored by gene expression for different cell types. These genes are differentially expressed across seven cell types. (c) The illustration of the discovery of different divergent genes. The L2 distance between g2 and g’2 is lower than the threshold at the early stage but exceeds the threshold at the late stage. While the L2 distance between g1 and g’1 exceeds the threshold for all stages. (d) The boxplot of normalized abstract correlation changes from excitatory neurons to macroglia between the top rewiring genes and the top conserved genes. Here we select 5% of genes with the largest L2 distance as the top rewiring genes and 5% of genes with the smallest l2 distance as the most conserved genes. The normalized L2 distance at different stages for EDG. We select one example EDG: PPARG, and its violin plot of gene expression indicates that PPARG is highly expressed in neurons. (f) The normalized L2 distance at different stages for LDG. We select one example LDG: RUNX2, and its violin plot of gene expression indicates that RUNX2 is highly expressed in microglia. "Middle" in iHerd refers to the first coarsening stage of the original network. With "Early", "Middle", and "Late" representing the coarsest, semi-coarse, and original networks, we can have deeper insights into gene behavior in different biological contexts.