Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies
Fig 2
UPSIDE distinguishes morphologically-distinct blood cell types in a heterogeneous population.
(A) Images of four different blood cell types were mixed together and passed through the UPSIDE workflow. Resultant shape and texture images were used to train concurrent VAEs. Output latent encodings were weighted relative to each other, concatenated, then projected onto a 2D plane using UMAP. (B) Dot plots show distribution of each cell type projected on 2D UMAP space made by UPSIDE. (C) 2D UMAP projection of the VAE-generated encodings that have been grouped into different morphological clusters using Louvain clustering algorithm. Representative brightfield cell crop images from the different clusters were listed. Scale bar represents 5 μm. (D) Cell type fractional composition within each cluster. A fixed number of cells from each cell type were sampled, and the cluster-wised cell type composition was calculated from this pooled population.