Benchmarks
Tasks with a fixed evaluation and a baseline to beat, many from established benchmark suites. Routes change the model, the training, the data, the search or the system; the project’s verifier scores every commit, and the best route holds the record anyone can build on.
Score genes by how spatially variable their expression is
Detecting spatially variable genes
Rank genes by spatial variability in 50 simulated spatial transcriptomics datasets where the true variability is known.
Simulate realistic spatial transcriptomics data
Realistic simulators for spatial transcriptomics
Simulate spatial transcriptomics data that match ten real datasets on 23 distributional and downstream-analysis metrics.
Estimate per-spot cell-type proportions from a single-cell reference
Cell-type decomposition of spatial transcriptomics spots
Estimate the cell-type proportions in each spot of simulated spatial transcriptomics data, given a single-cell reference.
Predict one modality's profile from another in held-out cells
Predicting one single-cell modality from another
Predict protein, chromatin accessibility or RNA profiles from the paired modality in held-out cells, on NeurIPS 2021–22 data.
Predict differential expression for held-out compound–cell type pairs
Predicting drug responses across blood cell types
Predict drug-induced expression changes in B and myeloid cells for held-out compounds, from responses measured in other blood cell types.
Project cell-type labels from labelled batches onto a held-out batch
Cell-type label projection from a reference atlas
Train a cell-type classifier on labelled batches and annotate a held-out batch, across six CELLxGENE atlases.
Reduce scRNA-seq data to a 2D embedding for visualisation
Two-dimensional embeddings for single-cell visualisation
Embed single-cell expression data in two dimensions while preserving neighbourhoods, distances and densities.
Denoise scRNA-seq count matrices
Denoising single-cell RNA-seq counts
Recover true expression from sparse scRNA-seq counts, scored by molecular cross-validation on held-out molecules.
Prioritise ligand–target cell type interactions
Inferring cell-cell communication from single-cell data
Rank ligand–target cell type interactions in a breast cancer atlas against a ground truth inferred from cytokine activity.
Integrate scRNA-seq batches while conserving biological variation
Batch integration of single-cell atlases
Remove batch effects from six single-cell atlases while keeping cell-type variation, scored on 13 metrics against control methods.