Fig. 2From: Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networksCellograph identifies treatment groups and distinguishes genes defining these groups on a human organoid dataset. A PHATE projection of learned latent space, with cells colored by treatment labels, probabilities of belonging to control or KPT-treated cells, clusters obtained by k-means clustering of the learned latent embedding with \(k=3\), and gene expression of GDF15 and KLK7. B Heatmap of top 25 weighted genes from parameterized gene weight matrix. C Heatmap of differentially expressed genes between clusters derived from Cellograph. D Compositional plot of predicted treatment groups from the softmax probabilities (\(z_{ij} > 0.5\)) (left) and cell types annotated by the original study (right) partitioned by clustersBack to article page