Fig. 2: ptalign decodes the ASA of GBMs using the adult NSC lineage as a reference.

a Left: UMAP embedding of integrated WT mouse v-SVZ NSC lineage scRNA-seq (n = 6 replicates), showing NSC differentiation trajectory through QAD stages. Center: v-SVZ lineage pseudotime acts as a reference into which query GBM cells are mapped by ptalign. Colored lines depict pseudotime-binned cells linked to their average position in UMAP. Right: UMAP of GBM PDX (n = 4 replicates) with ptalign-derived QAD-stages. Cycling cells (gray) are excluded from pseudotime analysis. Pie charts indicate QAD-stage proportions. b A pseudotime similarity metric derived from expression correlation along the v-SVZ lineage captures different stages and transitions by their characteristic similarity profiles. c In ptalign, a neural network is trained to predict v-SVZ lineage pseudotime based on similarity profiles derived from the masked reference. d Query tumor cell pseudotimes are assigned based on neural network predictions of v-SVZ similarity profiles. QAD-stage is derived based on the ptalign pseudotime value. e ptalign performance is quantified by DTW, while a permutation framework tests for alignment robustness. DTW values represent the transcriptome correlation of reference and tumor cells binned in pseudotime. f ptalign outlines a comparative view of tumor hierarchies, mapping patient samples within a single reference trajectory and enabling their comparison in that context. g scRNA-seq UMAP of n = 51 primary GBMs (Supplementary Data 2 and Supplementary Fig. 4), colored by patient. h Ternary plot of GBMs from (g) arranged by ptalign QAD-stage proportion, unveiling the underlying QAD-stage heterogeneity. Source data are provided as a Source Data file.