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nobusama17
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CDT2-data

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biology

genomics

bioinformatics

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
list<item: list<item: float>>
to
List(Value('float32'), length=512)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 87, in _generate_tables
                  yield Key(file_idx, batch_idx), cast_table_to_features(pa_table, self.info.features)
                                                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2285, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
                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 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              list<item: list<item: float>>
              to
              List(Value('float32'), length=512)
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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embeddings list	gene_names string
[ 0.001352875493466854, -0.009523511864244938, -0.05407179147005081, 0.05295247584581375, 0.024963119998574257, -0.02585672028362751, 0.07042928785085678, -0.058555252850055695, 0.054862458258867264, -0.03501478582620621, -0.07068057358264923, 0.03617383912205696, -0.008254666812717915, 0.03...	OR4F5
[ 0.07058318704366684, -0.06137524172663689, -0.011568957008421421, -0.04214022681117058, 0.03074839524924755, 0.08774320036172867, -0.05245037376880646, 0.026590978726744652, -0.007956607267260551, -0.04655521363019943, -0.0015242024092003703, 0.0268475990742445, -0.02038331888616085, 0.014...	SAMD11
[ -0.02234715037047863, 0.006158092059195042, 0.03308332711458206, -0.004810510203242302, -0.024905087426304817, -0.07092270255088806, 0.054653242230415344, -0.041291505098342896, -0.016094036400318146, -0.004289217758923769, -0.007040340453386307, 0.020908614620566368, -0.08633176982402802, ...	NOC2L
[ 0.01962483488023281, -0.03640452027320862, 0.0456574521958828, -0.055355291813611984, 0.009983060881495476, -0.06147432327270508, -0.02161540277302265, 0.013964179903268814, 0.019750241190195084, 0.016548439860343933, 0.016004391014575958, 0.009657949209213257, 0.01754002459347248, 0.05153...	KLHL17
[ -0.04232338070869446, 0.03568794205784798, 0.06245189532637596, 0.06680397689342499, -0.09544607251882553, 0.08651039749383926, 0.05475931987166405, 0.004726255778223276, -0.007338627707213163, -0.056442078202962875, -0.02007276378571987, 0.04288038983941078, -0.043433330953121185, 0.01128...	PLEKHN1
[ -0.033425912261009216, -0.02892191708087921, 0.0768435075879097, -0.036616984754800797, 0.07430356740951538, -0.019277235493063927, 0.03315439447760582, -0.022333210334181786, -0.048202402889728546, 0.04192231595516205, 0.03525567054748535, 0.10914374142885208, 0.009272199124097824, 0.0809...	PERM1
[ 0.03590874373912811, -0.002862729597836733, 0.015054324641823769, -0.05097123235464096, 0.05502422899007797, 0.008088524453341961, 0.06312956660985947, -0.03612089157104492, 0.03415500000119209, 0.02832110784947872, -0.022212957963347435, -0.00040276895742863417, -0.01763547956943512, -0.0...	HES4
[ -0.031369399279356, 0.011317543685436249, -0.026589328423142433, 0.07066091150045395, 0.019969487562775612, -0.04012702777981758, 0.06504164636135101, -0.006696357857435942, 0.054194387048482895, 0.024737777188420296, -0.03670543059706688, -0.02082456834614277, -0.06148063391447067, -0.004...	ISG15
[ -0.03857249766588211, -0.04542655125260353, 0.03946270793676376, -0.003936372697353363, 0.0070471251383423805, 0.009801584295928478, 0.02875147946178913, -0.043353766202926636, 0.023637831211090088, 0.052778132259845734, 0.08118009567260742, -0.028116295114159584, 0.04204206541180611, -0.0...	AGRN
[ -0.0215851329267025, 0.04138462617993355, 0.0900764912366867, 0.04712744802236557, -0.01224204245954752, 0.01673630066215992, 0.004139477387070656, -0.013818349689245224, -0.04589757323265076, -0.01471775397658348, -0.04616600275039673, -0.05199866741895676, -0.02286529541015625, -0.006422...	RNF223
[ -0.040783174335956573, -0.024996314197778702, -0.03046107478439808, 0.024974441155791283, -0.01632026582956314, -0.01756705716252327, 0.06072530150413513, -0.06786185503005981, -0.01748485118150711, 0.07020499557256699, 0.07864293456077576, -0.007049329578876495, 0.05084874853491783, 0.023...	C1orf159
[ -0.015511286444962025, -0.019041797146201134, -0.0494326651096344, -0.04331563040614128, 0.018853841349482536, 0.038298022001981735, 0.06566962599754333, -0.005717407912015915, -0.08861452341079712, 0.00807254109531641, 0.027548613026738167, -0.02618682011961937, -0.012936646118760109, 0.0...	TTLL10
[ 0.035143375396728516, 0.00039390975143760443, -0.06924386322498322, -0.03349749743938446, -0.009472040459513664, -0.02460530586540699, 0.012908946722745895, 0.031127236783504486, 0.0968828797340393, -0.07601948082447052, -0.0038184651639312506, 0.06907182186841965, 0.06185617670416832, 0.0...	TNFRSF18
[ -0.028040628880262375, 0.015036636963486671, 0.015110660344362259, 0.0010332296369597316, -0.040521617978811264, 0.04879506677389145, 0.10565763711929321, 0.01856708526611328, 0.06133867800235748, 0.026024090126156807, -0.018294215202331543, -0.03574180230498314, 0.00844360888004303, -0.00...	TNFRSF4
[ 0.04402223601937294, -0.02186136692762375, -0.0010823707561939955, 0.023864328861236572, -0.05890693888068199, 0.018372241407632828, 0.018594704568386078, -0.054233964532613754, 0.0600605383515358, 0.009192943572998047, 0.05387506261467934, -0.06888148188591003, 0.005348770413547754, -0.00...	SDF4
[ 0.013077393174171448, -0.02086571604013443, 0.038029853254556656, 0.0012146761873736978, 0.040323324501514435, -0.010181454941630363, -0.02172306925058365, 0.007240517996251583, 0.007289928384125233, 0.0006136192241683602, 0.016488781198859215, -0.026101768016815186, 0.02501760423183441, 0...	B3GALT6
[ -0.054752167314291, -0.041882045567035675, 0.037762802094221115, -0.02217266708612442, 0.01345647033303976, 0.04904264584183693, 0.048112377524375916, -0.013103523291647434, -0.03572763502597809, 0.0007111952872946858, 0.026774493977427483, -0.008611930534243584, -0.02736525982618332, 0.07...	C1QTNF12
[ -0.012824359349906445, -0.05287139490246773, 0.010750668123364449, 0.0014497425872832537, -0.017095694318413734, -0.06256240606307983, 0.015225081704556942, 0.032327279448509216, 0.026799116283655167, -0.023573661223053932, -0.05118025094270706, 0.03855108842253685, 0.021451491862535477, -...	UBE2J2

End of preview.

CDT2-data

Data files for Central Dogma Transformer II (CDT-II).

Paper: Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms

Published in Bioinformatics Advances (2026). DOI: 10.1093/bioadv/vbag268

Files

File	Description	Size
`morris_celllevel_effects_2361.h5`	Cell-level perturbation effects (TSS, 2,361 genes)	41 MB
`morris_snp_celllevel_effects_2361.h5`	Cell-level perturbation effects (SNP, 2,361 genes)	34 MB
`k562_gene_embeddings_aligned.h5`	Gene embeddings from scGPT (2,360 genes)	4.4 MB
`cdt_morris_celllevel_best.pt`	Trained CDT-II model weights	80 MB
`morris_28genes_enformer.h5`	Enformer DNA embeddings (TSS, 28 genes)	277 MB
`morris_snp_enformer.h5`	Enformer DNA embeddings (SNP)	4.8 GB

Enformer Embeddings

The Enformer embeddings above are included. They were generated with the notebooks in the CDT2 repository, which can also be used to regenerate them:

morris_28genes_enformer.h5 - notebooks/embeddings/Morris_28genes_Enformer.ipynb
morris_snp_enformer.h5 - notebooks/embeddings/Morris_SNP_Enformer.ipynb

These notebooks run Enformer inference on Google Colab and save the embeddings to Google Drive.

Usage

from huggingface_hub import hf_hub_download

# Download cell-level effects
effects_path = hf_hub_download(
    repo_id="nobusama17/CDT2-data",
    filename="morris_celllevel_effects_2361.h5",
    repo_type="dataset"
)

# Download model weights
model_path = hf_hub_download(
    repo_id="nobusama17/CDT2-data",
    filename="cdt_morris_celllevel_best.pt",
    repo_type="dataset"
)

Data Source

Morris STING-seq v2 dataset (60,505 K562 cells, 447 perturbation loci)
Reference: Morris et al. Science 2023

Citation

@article{ota2026cdtii,
  title={Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms},
  author={Ota, Nobuyuki},
  journal={Bioinformatics Advances},
  pages={vbag268},
  year={2026},
  doi={10.1093/bioadv/vbag268}
}

License

MIT License

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Total file size:

5.56 GB

Paper for nobusama17/CDT2-data

Central Dogma Transformer II: An AI Microscope for Understanding Cellular Regulatory Mechanisms

Paper • 2602.08751 • Published 10 days ago