Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Curated Single-Cell Drug Perturbation Benchmarks
This release contains 20 single-cell drug-perturbation benchmarks linked to 20 distinct papers. Each benchmark includes one control expression matrix, one treated ground-truth matrix, and a JSON test specification. The selection prioritizes broad target-gene panels and agreement between the encoded target directions and the main conclusions of the source paper.
Release status and license
The benchmark metadata is prepared for release, but redistribution rights for the derived H5AD files must be confirmed from the original studies before making the repository public. license: other is intentionally conservative and does not grant rights beyond those of the source datasets. Users remain responsible for complying with the original study terms and citing the corresponding paper.
Files
README.md
benchmarks.csv
release_manifest.csv
release_audit.json
data/
<benchmark_id>/
control.h5ad
ground_truth.h5ad
test_case.json
The 60 core data files total 30.4 GiB. release_manifest.csv records the exact byte size and SHA256 digest of each file. Local source paths are excluded from the public manifest.
Benchmarks
| Rank | Benchmark | PMID | Perturbation | Tissue | Targets | Direction agreement |
|---|---|---|---|---|---|---|
| 1 | 38937474_01 |
38937474 | osimertinib | Lung | 34 | 32/34 (94.1%) |
| 2 | 38895265_01 |
38895265 | Paclitaxel | Breast | 18 | 18/18 (100.0%) |
| 3 | 34591417_01 |
34591417 | vemurafenib | Skin | 14 | 14/14 (100.0%) |
| 4 | 40766395_01 |
40766395 | Brefeldin A | Liver | 14 | 14/14 (100.0%) |
| 5 | 37086265_01 |
37086265 | etoposide | Lung | 18 | 16/18 (88.9%) |
| 6 | 33712615_01 |
33712615 | erlotinib | Lung | 19 | 15/19 (78.9%) |
| 7 | 32846134_01 |
32846134 | 5-fluorouracil | Colon | 9 | 9/9 (100.0%) |
| 8 | 37732484_01 |
37732484 | paclitaxel | Aorta | 11 | 10/11 (90.9%) |
| 9 | 36318267_01 |
36318267 | estradiol | Breast | 8 | 8/8 (100.0%) |
| 10 | 36553506_01 |
36553506 | panobinostat | Brain | 12 | 10/12 (83.3%) |
| 11 | 35410383_01 |
35410383 | Fluorouracil | Breast | 6 | 6/6 (100.0%) |
| 12 | 41871169_01 |
41871169 | panobinostat | B lymphoblast | 6 | 6/6 (100.0%) |
| 13 | 38652658_01 |
38652658 | ispinesib | Brain | 9 | 8/9 (88.9%) |
| 14 | 36382181_01 |
36382181 | enzalutamide | Prostate | 9 | 7/9 (77.8%) |
| 15 | 32094658_01 |
32094658 | latrunculin A | Pancreas | 4 | 4/4 (100.0%) |
| 16 | 38272949_02 |
38272949 | GW3965 | Brain | 4 | 4/4 (100.0%) |
| 17 | 38589664_01 |
38589664 | cisplatin | Stomach | 3 | 3/3 (100.0%) |
| 18 | 39803533_01 |
39803533 | TCDD | Skin | 3 | 3/3 (100.0%) |
| 19 | 40166195_01 |
40166195 | doxorubicin | Breast | 3 | 3/3 (100.0%) |
| 20 | 34857732_01 |
34857732 | GSK126 | Prostate | 8 | 5/8 (62.5%) |
Across the 20 benchmarks there are 212 within-benchmark unique target genes, of which 195 (92.0%) match the encoded direction under the strict aggregate check described below.
Direction validation
For each target gene, the release audit compares the mean of the treated matrix (ground_truth.h5ad) with the mean of the control matrix (control.h5ad):
relative_effect = (treated_mean - control_mean) /
(abs(treated_mean) + abs(control_mean))
Values at least 0.1 are classified as UP, values at most -0.1 as DOWN, and intermediate values as NS. This is an aggregate dataset-level consistency check, not a dose-stratified or time-stratified statistical significance test.
H5AD content
X contains the processed expression matrix. The files also retain observation and variable metadata and, where available, a counts layer. Consult test_case.json for target genes, expected relation, perturbation groups, time groups, and cell type for each test.
Audit notes
All hard integrity checks passed. Two JSON test cases contain no target genes and are therefore not evaluable. Several JSON time/dose labels are not present as literal categorical values in obs; use the recorded condition, orig.ident, and sample_id fields together with the JSON specification. One benchmark has six barcodes shared between the separate control and treated files; use sample_id or the split name when concatenating. One benchmark stores the same gene set in a different column order between splits; downstream joins must align by var_names, never by column position.
Data leakage warning
Benchmarks derived from the same source context may not represent statistically independent samples. Keep benchmark_id and PMID grouping intact when constructing train/test splits; random cell-level splitting can cause leakage.
Citation
Each row in benchmarks.csv contains the PMID and paper title. Cite the relevant original papers when using individual benchmarks.
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