Each task pairs a problem (e.g. graph clustering) with a dataset and a reference algorithm. Pick one to see the leaderboard, baseline metrics, and submission instructions.
Build the cell-cell kNN graph faster than scanpy.pp.neighbors while keeping edge-set Jaccard >= 0.9 vs the reference. Train on PBMC 3K (2.6K cells), evaluated on a held-out 50K-cell synthetic dataset to test scaling.
Evolve Leiden graph clustering to run faster while maintaining clustering quality. Train on PBMC 3K (~2.6K cells, fast iteration); evaluated on a held-out 50K-cell synthetic PBMC dataset that preserves the original cluster structure.
Build a peak-calling implementation that matches or outperforms MACS3 on ATAC-seq data. Train on GM12878 scATAC-seq (111M reads).