Benchmarks

Batch integration embed
Removing batch effects while preserving biological variation (embedding output)
3 datasets · 32 methods · 8 control methods · 10 metrics

Batch integration feature
Removing batch effects while preserving biological variation (feature output)
3 datasets · 18 methods · 7 control methods · 11 metrics

Batch integration graph
Removing batch effects while preserving biological variation (graph output)
3 datasets · 40 methods · 5 control methods · 4 metrics

Cell-Cell Communication Inference (Ligand-Target)
Detect interactions between ligands and target cell types
1 datasets · 14 methods · 2 control methods · 2 metrics

Cell-Cell Communication Inference (Source-Target)
Detect interactions between source and target cell types
1 datasets · 14 methods · 2 control methods · 2 metrics

Denoising
Removing noise in sparse single-cell RNA-sequencing count data
3 datasets · 11 methods · 2 control methods · 2 metrics

Dimensionality reduction for visualisation
Reduction of high-dimensional datasets to 2D for visualization & interpretation
4 datasets · 23 methods · 3 control methods · 10 metrics

Foundation models
Modelling of single-cells to perform multiple tasks.
Recent developments in deep-learning have led to the creation of several “foundation models” for single-cell data. These are large models that have been trained on data from…

Label Projection
Automated cell type annotation from rich, labeled reference data
8 datasets · 16 methods · 2 control methods · 3 metrics

Multimodal Data Integration
Alignment of cellular profiles from two different modalities
3 datasets · 5 methods · 2 control methods · 2 metrics

Perturbation Prediction
Predicting how small molecules change gene expression in different cell types.
1 datasets · 6 methods · 6 control methods · 5 metrics

Spatial Decomposition
Calling cell-type compositions for spot-based spatial transcriptomics data
7 datasets · 13 methods · 2 control methods · 1 metrics

Spatial Simulators
Assessing the quality of spatial transcriptomics simulators.
10 datasets · 7 methods · 2 control methods · 29 metrics

Spatially Variable Genes
Spatially variable genes (SVGs) are genes whose expression levels vary significantly across different spatial regions within a tissue or across cells in a spatially structured context.
50 datasets · 14 methods · 2 control methods · 1 metrics
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Additional information