jang1563/genelab-benchmark

SpaceBio-Bench / GeneLab Benchmark v7.1.3 Public Fold Package

Corrections (September 2026; applied in v7.1.3, 2026-09-27). Two labelling errors in the released benchmark are corrected. (1) The gastrocnemius task (A2) lists a third mission as "RR-9"; those eight samples (OSD-326) come from the SpaceX-10 mission (Rodent Research-4) and are quadriceps femoris, not gastrocnemius. RR-9 contributes liver and thymus only. (2) The eye task (A6) evaluated the OSD-397 samples as a separate mission; they belong to RR-1 and share animals with OSD-100, so the task has two mission-held-out folds, not three. This card and RESULTS_SUMMARY.md use the corrected labels; fold directory names keep the historical labels (fold_RR-9_test, fold_OSD-397_test). See the changelog below for the other v7.1.3 corrections and the MAQC 2026 abstract erratum. The source code repository is private while the corrected release is prepared; code and fold definitions are available on request (contact: JangKeun Kim). Every other task and the mission-held-out design are unaffected.

Processed mission-held-out transcriptomics folds for evaluating whether machine-learning and foundation-model methods generalize spaceflight biological signatures across missions.

Public status: v7.1.3 correction patch over canonical v7.1 results (2026-09-27; earlier patch: v7.1.2 public-card/metadata patch, 2026-06-16)

Dataset freeze: 2026-03-01

Patch scope: documentation corrections over canonical v7.1 results (mission labels for OSD-326 and OSD-397, the v4 headline table regenerated from its result JSON, sample scope and averaging rules, fine-tuning epoch selection, the skin RR-7 open-validation row, fGSEA direction statements) and the MAQC 2026 abstract erratum. It does not introduce new benchmark result generation.

Code and full documentation: https://github.com/jang1563/GeneLab_benchmark

Maintainer / citation author: JangKeun Kim, Weill Cornell Medicine.

What Is In This Dataset

This Hugging Face dataset contains self-contained public fold packages from the GeneLab Benchmark v1-v7 surface. Each fold holds out one mission as the test set and provides all files needed to train on the remaining missions and evaluate on the held-out mission.

Public package item	Description
train_X.csv, test_X.csv	Sample-by-gene expression matrices
train_y.csv, test_y.csv	Binary labels: 1 = Flight, 0 = Ground
train_meta.csv, test_meta.csv	Sample-level metadata used for fold auditing
fold_info.json	Held-out mission, train missions, and sample-count audit metadata
selected_genes.txt	Fold-specific genes selected from training missions only
task_info.json	Task-level metadata and source summary

The web Dataset Viewer is disabled because these are high-dimensional sample-by-gene matrices plus JSON artifacts. Use direct downloads for reliable access.

Public Fold Layout

genelab-benchmark/
├── A2_gastrocnemius_lomo/
│   ├── task_info.json
│   ├── fold_RR-1_test/
│   ├── fold_RR-5_test/
│   └── fold_RR-9_test/
├── A4_thymus_lomo/
│   ├── task_info.json
│   ├── fold_MHU-1_test/
│   ├── fold_MHU-2_test/
│   ├── fold_RR-6_test/
│   ├── fold_RR-9_test/
│   └── fold_RR-23_holdout/      # retrospective open validation; labels public
├── A5_skin_lomo/
│   ├── task_info.json
│   ├── fold_MHU-2_test/
│   ├── fold_RR-6_test/
│   ├── fold_RR-7_test/
│   └── fold_RR-7_holdout/       # identical copy of fold_RR-7_test; labels public
├── A6_eye_lomo/
│   ├── task_info.json
│   ├── fold_RR-1_test/
│   ├── fold_RR-3_test/
│   └── fold_OSD-397_test/
├── v4/evaluation/
├── v5/evaluation/
└── v6/evaluation/

fold_OSD-397_test is the third A6 fold directory. Its samples belong to RR-1 (corrected 2026-09-25; they share animals with OSD-100), so A6 has two mission-held-out folds.

The two historical _holdout directories also contain public test_y.csv files. They support retrospective reproducibility, not blind evaluation. fold_RR-7_holdout repeats fold_RR-7_test exactly; only fold_RR-23_holdout holds a mission outside the LOMO folds. See the benchmark integrity note.

Scope

Dimension	Coverage
Full v1-v7 benchmark surface	8 tissues
Public source catalog	24+ NASA OSDR accessions
Processed sample scope	549 profiles in the v1 LOMO tasks A1–A6 (22 folds; 565 with the RR-23 open-validation fold) and 792 in the v4 8-tissue evaluation; counts are RNA-seq profiles, not animals
v4 multi-method evaluation	8 tissues x 8 classifiers x 4 feature types = 256 evaluations
Public HF fold package	4 reviewer-facing LOMO tasks plus selected result artifacts

The full GitHub benchmark also includes historical v2-v7 extensions for temporal dynamics, cross-species analysis, single-cell and spatial pilots, foundation-model comparisons, graph/network baselines, and biological interpretation layers. This HF repository is optimized for processed dataset access; GitHub is the complete methods, code, and release-documentation surface.

Download Example

from huggingface_hub import hf_hub_download
import pandas as pd

repo_id = "jang1563/genelab-benchmark"
fold = "A5_skin_lomo/fold_RR-7_test"

def hf_csv(name):
    return pd.read_csv(
        hf_hub_download(
            repo_id=repo_id,
            filename=f"{fold}/{name}",
            repo_type="dataset",
        ),
        index_col=0,
    )

train_X = hf_csv("train_X.csv")
train_y = hf_csv("train_y.csv").iloc[:, 0]
test_X = hf_csv("test_X.csv")
test_y = hf_csv("test_y.csv").iloc[:, 0]
test_meta = hf_csv("test_meta.csv")

print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)

Download a complete task:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="jang1563/genelab-benchmark",
    repo_type="dataset",
    allow_patterns="A5_skin_lomo/**",
    local_dir="./data/genelab-benchmark",
)

File Contract

File	Contract
Feature matrices	Rows are sample IDs; columns are Ensembl mouse gene IDs
Expression values	Log2(DESeq2 size-factor normalized counts + 1)
Gene selection	Top 75th percentile variance, computed on training missions only
Labels	Binary Flight/Ground labels
Metadata	Sample and fold metadata for auditability

The fold design prevents test-mission leakage by applying variance filtering inside each training split.

Evaluation Summary

The canonical result source is docs/CANONICAL_RESULTS_V7_1.md in the GitHub repository.

Result surface	Takeaway
Multi-method benchmark	PCA-LR is the strongest gene-level baseline in v4: mean AUROC 0.776 over 8 tissues (0.753 over the 6 LOMO tissues).
Best tissue rows	Highest of 32 method-feature rows per tissue, selected after the fact: LOMO thymus 0.948, gastrocnemius 0.898, kidney 0.829, eye 0.823, skin 0.819, liver 0.766; single-mission colon 0.921 and lung 0.901 use 5-fold CV, not LOMO.
Cross-mission transfer	Thymus and gastrocnemius show the strongest mission-transfer signal; liver and kidney are harder.
Pathway features	Pathway representations rescue some weaker gene-level tissues, especially kidney and eye.
Foundation models	Tested gene-expression foundation models underperform tuned classical baselines on small-n bulk RNA-seq mission shift. Fine-tuned scGPT and Mouse-Geneformer fold AUROCs keep the best of 10 epochs scored on the held-out test mission itself (no inner validation split; scripts/scgpt_finetune.py, scripts/geneformer_finetune.py), so they are optimistic.
Retrospective open validation	Thymus RR-23 AUROC 0.905 (n=16); labels are public. The skin RR-7 split is a copy of the LOMO RR-7 fold; its recorded 0.885 does not reproduce (a converged refit gives the LOMO value 0.805), so it is not additional evidence.

Intended Use

Use this dataset to:

• evaluate spaceflight transcriptomics classifiers under mission-held-out shift;
• compare classical ML, foundation-model, and adapter methods on fixed folds;
• test preprocessing or feature representations without changing test missions;
• reproduce public benchmark summaries from the GitHub repository.

For full methods and release status, use the GitHub documentation and release manifest.

Release Labels

Surface	Public label
v7.1 GeneLab Benchmark	Canonical historical result surface and citation target
v7.1.2 public-card patch	Documentation and metadata patch over v7.1 results (2026-06-16)
v7.1.3 correction patch	Label, headline-table and documentation corrections over v7.1 results; MAQC 2026 abstract erratum (2026-09-27)

This HF dataset card describes the v7.1 public fold package with the v7.1.3 correction patch.

Changelog

v7.1.3 (2026-09-27), correction patch.

• Mission labels: the A2 fold archived as RR-9 is the SpaceX-10 / RR-4 quadriceps study (OSD-326); the A6 OSD-397 samples belong to RR-1, so A6 has two mission-held-out folds.
• v4 headline table regenerated from v4/evaluation/M1_summary.json, with a fixed PCA-LR column, a post hoc best-row column and the evaluation scheme (colon and lung use 5-fold stratified CV); the previous table held two values that are not v4 rows and three wrong method labels.
• Sample scope stated as 549 profiles in 22 LOMO folds for the v1 tasks A1–A6 (565 with the RR-23 fold; 792 in the v4 evaluation); the six-tissue classical reference mean (0.758) is a fixed per-tissue model, not PCA-LR.
• Fine-tuned scGPT and Mouse-Geneformer values disclosed as best-of-10-epoch selections on the held-out test mission.
• Skin fold_RR-7_holdout documented as a copy of the LOMO RR-7 fold; its recorded logistic-regression AUROC (0.885) does not reproduce, and a converged refit gives 0.805, the LOMO value.
• Pathway-direction statements corrected to the flight-positive fGSEA sign convention (files regenerated on 2026-08-14).

Erratum (MAQC 2026 abstract). The abstract "From Reproducible Pipelines to Reproducible Claims: An Audit of Model Selection and Aggregation in Spaceflight Omics" (Kim and Mason, submitted to MAQC 2026 on 19 August 2026) reported results on the six-tissue task surface of this dataset as released at the time (549 profiles, 22 leave-one-mission-out task folds): fixed PCA-logistic regression six-tissue macro AUROC 0.730; scGPT 0.666 with the best held-out test epoch per fold and 0.599 at a fixed epoch 10; Mouse-Geneformer 0.476 and 0.458; thymus PCA-logistic regression 0.923 as the mean of mission-level AUROCs and 0.631 pooled out of fold. As the abstract stated, the fixed-epoch values are a post hoc sensitivity analysis, not nested epoch selection. The values were computed correctly from the files released at the time, but three problems found afterwards affect them: (1) the scGPT and Mouse-Geneformer inputs were z-scored expression values, while both tokenizers expect raw counts; (2) the gastrocnemius task's third mission is the SpaceX-10 / Rodent Research-4 quadriceps study (OSD-326), not RR-9 gastrocnemius, and the eye task has two mission-held-out folds, not three, because the OSD-397 samples belong to RR-1 and share animals with OSD-100; (3) three MHU-2 thymus flight profiles carry swapped condition labels: the original BioSample records show that the profiles archived as MHU2_FLT_1G_Rep1-3 are microgravity and the uG-named profiles are artificial gravity. Read the abstract's numbers as results on the pre-correction surface; they are not comparable with later results. The reanalysis (raw-count inputs, nested epoch selection, corrected labels, a recorded evaluation contract) is reported separately.

v7.1.2 (2026-06-16). Public-card, citation, and metadata patch over canonical v7.1 results.

Citation

Please cite the software and benchmark using the GitHub CITATION.cff metadata.

@dataset{kim2026genelab,
  title = {SpaceBio-Bench / GeneLab Benchmark: Mission-Held-Out Spaceflight Transcriptomics Benchmark},
  author = {Kim, JangKeun},
  year = {2026},
  url = {https://huggingface.co/datasets/jang1563/genelab-benchmark},
  note = {v7.1.3 correction patch over canonical v7.1 results; data freeze 2026-03-01}
}

Source data: NASA Open Science Data Repository (OSDR), https://osdr.nasa.gov/bio/repo/.

License

• Processed dataset package: CC-BY-4.0
• Code: MIT, in the GitHub repository
• Source data: NASA OSDR public data; follow individual source-dataset terms