Fig. 4: Trajectory inference with STORIES in mouse gliogenesis.
From: STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport
a, 3D representation of the potential landscape learned with STORIES. The x and y axes are Isomap coordinates, and the z axis is an interpolation of the potential. Colors represent RGCs that differentiate into either NeuBs or GlioBs. b, Visual representation of cell–cell transitions computed using CellRank from STORIES’s velocity vectors. c, 2D visualization of a mouse slice at E16.5, with cells colored by their cell-type annotation. d, 2D visualization of a mouse midbrain slice at E16.5, with RGCs colored by their predicted probability of becoming GlioBs. e, Smoothed gene expression for Mki67 and Aldh1l1 along the potential computed by STORIES. The blue line is a spline regression of expression from potential. f, Normalized gene expression regressed using a spline model along the potential computed by STORIES. Genes are ordered by the potential for which they achieve maximum expression. g, Smoothed gene expression for Tuba1b and Glis3 along the potential computed by STORIES. The blue line is a spline regression of expression from potential. h, Enrichment scores of transcription factors targeting candidate driver genes. A one-sided Wilcoxon rank-sum test is used to report P values.
