Extended Data Fig. 8: Robustness of cellular landscape modelling by BEYOND.
From: Cellular communities reveal trajectories of brain ageing and Alzheimer’s disease
(a) 3D PHATE embedding of all 437 snRNA-seq participants, coloured by clustering of participants based on their cellular environments (Methods). (b) Distinct patterns of subpopulations along the cellular landscape manifold, showing additional subpopulations to those of Fig. 5c. Participants (dots) are coloured by the locally smoothed proportion of each subpopulation. (c) Robustness of the cellular landscape to the embedding method and set of subpopulations used in BEYOND. Participants are coloured by the locally smoothed subpopulation proportion. (d-e) Visualizing fitted pseudotime, trajectories and Shannon entropy of trajectory probabilities outputted by: (d) VIA (n = 437 participants), and (e) Palantir algorithms (n = 386, excluding participant-clusters #9 and #10 in a).(f) Robustness of trajectories and pseudotime predictions using different algorithms (over the overlapping n = 386 participants). (Top) Pseudotime assigned for each individual by Palantir compared to VIA. (Bottom) Trajectory probabilities Pearson correlations. Corrected for multiple hypothesis testing (BH). (g) Participants’ trajectory probabilities entropy drop along pseudotime, in the Palantir model. Dots are coloured by prAD minus ABA trajectory probabilities. The grey area indicates a pseudotime range (0, 0.11) in which the two trajectories are not well separated. (h) Trait-dynamics of AD-related traits along the pseudotime in each of the inferred trajectories, showing the datapoints used in fitted curves and error bands showing 0.95 CI (Methods). n = 386 participants. As in Fig. 5f. (i-j) Validation of cellular landscapes and trajectories using the Replication cohort (n = 673 non-overlapping participants, with the 62 reliable CelMod bulk-predicted subpopulations proportions to represent cellular environments). (i) The Palantir model over replication landscape as in (e). (j) Trajectory probabilities entropy drop as in (g) but over the replication landscape.
