Benchmarking single-cell dynamics: code, model checkpoints, and processed single-cell datasets

Code, trained model checkpoints, and processed single-cell .h5ad datasets for a benchmark of single-cell dynamics / trajectory-inference / optimal-transport flow methods.

Code

• benchmarking_code.tar.gz — the full benchmarking pipeline: per-method prepare/train/evaluate scripts, the cord-blood benchmark driver and its SLURM job definitions, cross-method aggregation (fate accuracy, Wasserstein-2 global and per-clone), and the generated report tables and figures under reports/. Includes environment.yml, pip requirements, and a MANIFEST.txt recording the originating git commit and per-file SHA-256 checksums.

Cord-blood dataset (rebuild, not redistributed)

The cord-blood expression data is not deposited here. It is available from doi:10.6084/m9.figshare.27908142 (CordBlood_Refine.zip → adata_update.h5ad, CC BY 4.0). That release carries the same 24,885 cells and the same expression matrix, but not the annotations and embeddings this benchmark trains on.

cordblood_sidecar.tar.gz supplies exactly those missing fields — 6 obs columns (Well, Annotation, Time_point, label_man, split, timepoint_tx_days), 8 obsm embeddings (diffusion-map eigenvectors, scaled PCA, and the delta-embeddings), 2 obsp graphs, and 9 uns entries (population priors and the PCA/DM scalers). All are keyed by cell barcode, so the join does not depend on row order.

To reconstruct the exact h5ad used in the paper:

python scripts/cordblood/data_prep/01_rebuild_from_figshare.py 
 --sidecar cordblood_sidecar --out data/cordblood_addpop.h5ad
python scripts/cordblood/data_prep/02_verify_rebuild.py 
 --ref data/cordblood_addpop.h5ad --new data/cordblood_rebuilt.h5ad

The rebuild has been verified to reproduce the original h5ad exactly (76/76 equality checks: X, raw, layers, every obs column, obsm, obsp and uns key).

Checkpoints (one tarball per method)

• DeepRUOT_checkpoints.tar.gz
• MIOFlow_checkpoints.tar.gz
• otcfm.tar.gz (OT-CFM)
• pdp+_checkpoints.tar.gz (pseudodynamics+)
• PRESCIENT_checkpoint.tar.gz
• scDiffeq-checkpoints.tar.gz
• sf2m_checkpoints.tar.gz (SF2M)
• TIGON_checkpoints.tar.gz
• TNJ_checkpoints.tar.gz (TrajectoryNet)

Datasets (AnnData / HDF5)

• klein_addpop.h5ad — the processed LARRY clonal dataset (126,861 cells), the main cross-method benchmarking dataset.
• meahr_monocle.h5ad
• tom_pos.h5ad
• synthetic_FP.h5ad<
  

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