Zev Gartner
@zevgartner.bsky.social
Building tissues to understand how tissues build themselves
7. The key innovation leading to this performance gain is a probabilistic, dataset- and neighborhood-aware sampling strategy tailored for self-supervised contrastive learning, enabling the generation of coherent, denoised and high-resolution latent cell representations across diverse datasets:
March 20, 2025 at 6:08 PM
7. The key innovation leading to this performance gain is a probabilistic, dataset- and neighborhood-aware sampling strategy tailored for self-supervised contrastive learning, enabling the generation of coherent, denoised and high-resolution latent cell representations across diverse datasets:
5. An intestinal development atlas demonstrated CONCORD's ability to resolve complex topologies, including differentiation trajectories intertwined with cell cycle loops (click here for interactive 3D UMAP! qinzhu.github.io/Concord_docu...):
March 20, 2025 at 6:08 PM
5. An intestinal development atlas demonstrated CONCORD's ability to resolve complex topologies, including differentiation trajectories intertwined with cell cycle loops (click here for interactive 3D UMAP! qinzhu.github.io/Concord_docu...):
4. We validated CONCORD on an embryonic atlas of C. elegans and C. briggsae from @jisaacmurray. CONCORD captured known bifurcations, resolved subtle cell states, and uncovered lineage convergence events missed by other methods (click here for interactive 3D UMAP! qinzhu.github.io/Concord_docu...):
March 20, 2025 at 6:08 PM
4. We validated CONCORD on an embryonic atlas of C. elegans and C. briggsae from @jisaacmurray. CONCORD captured known bifurcations, resolved subtle cell states, and uncovered lineage convergence events missed by other methods (click here for interactive 3D UMAP! qinzhu.github.io/Concord_docu...):
3. We evaluated CONCORD on datasets with topologies such as continuous trajectories, loops, clusters and hierarchical trees. Using scIB metrics, geometric measures, and TDA, we assessed its ability to preserve biological structure. CONCORD outperforms existing methods:
March 20, 2025 at 6:08 PM
3. We evaluated CONCORD on datasets with topologies such as continuous trajectories, loops, clusters and hierarchical trees. Using scIB metrics, geometric measures, and TDA, we assessed its ability to preserve biological structure. CONCORD outperforms existing methods:
1. Struggling to integrate single-cell datasets? Finding it hard to resolve clear differentiation trajectories? Reveal the underlying structure in your data with CONCORD.
March 20, 2025 at 6:08 PM
1. Struggling to integrate single-cell datasets? Finding it hard to resolve clear differentiation trajectories? Reveal the underlying structure in your data with CONCORD.