Flexynesis Benchmark Datasets
| Dataset key | Biology | Modalities | Samples | Task types available |
|---|---|---|---|---|
dataset1 |
Cancer drug response (CCLE/GDSC cell lines) | gex, cnv |
~950 / 240 | Regression (drug IC50 per compound) |
dataset2 |
Microsatellite instability (MSI) | gex, meth |
~380 / 95 | Binary classification (MSI-H vs MSS) |
lgggbm_tcga_pub_processed |
Brain tumours: LGG + GBM (TCGA) | mut, cna |
556 / 238 | Classification, survival, regression |
brca_metabric |
Breast cancer (METABRIC) | gex, cna |
~1390 / 595 | Classification, survival, regression |
singlecell_bonemarrow |
Bone marrow single-cell RNA | gex |
~7500 / 2500 | Classification, unsupervised |
Note on single-cell data: flexynesis was designed for bulk multi-omics data (patient cohorts, cell lines) — not single-cell RNA-seq. It has no built-in handling for the sparsity, scale, or batch structure typical of scRNA-seq. The singlecell_bonemarrow dataset is included as a benchmark curiosity and works well for supervised cell type classification (where cell type labels are available), but flexynesis is not the right tool for unsupervised single-cell analysis, trajectory inference, or integration of large scRNA-seq atlases. For those tasks, use Scanpy/Seurat/scVI instead. If the user's question is specifically about supervised classification of single-cell data with known labels, it is worth trying.
All datasets hosted at https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/
Reference: Uyar et al., Nature Communications 2025 — https://doi.org/10.1038/s41467-025-63688-5