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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1276 new columns ({'TRAPPC3', 'MARCO', 'FCER2', 'LRRC15', 'CX3CR1', 'CYTIP', 'TM4SF18', 'SDC4', 'PPP1R1B', 'APOB', 'CES1', 'NOTCH1', 'SLC18A2', 'CA4', 'SERHL2', 'HMGB2', 'STEAP4', 'SSR2', 'ALDH1B1', 'LAMC3', 'SFTA2', 'BATF3', 'FCN2', 'PLCG2', 'SOD3', 'RNASE1', 'SNTN', 'CFHR1', 'POSTN', 'RETNLB', 'FRZB', 'CCL11', 'CTLA4', 'KRT20', 'IL22RA2', 'SFRP2', 'CCDC39', 'CXCR4', 'CD79A', 'SNCA', 'TREM2', 'FBLN2', 'CREB3L1', 'PAPLN', 'C1R', 'PAX5', 'PTPRC', 'E2F1', 'CCL13', 'FGFBP2', 'C11orf96', 'CXCR5', 'IFNAR1', 'KLF6', 'CAVIN1', 'ANXA3', 'SKP1', 'DUOX1', 'MT1A', 'CD93', 'NCAM1', 'CDC42EP1', 'CD96', 'MMP12', 'GPRIN3', 'SSR3', 'COL1A1', 'HLA-B', 'IL4R', 'GPX2', 'CADM2', 'FAM107B', 'ARL14', 'SERPINE1', 'HAMP', 'OLFM4', 'TRDV1', 'LGR6', 'POLR2J3', 'SHANK3', 'MAF', 'CDK1', 'BTNL9', 'CD4', 'CENPF', 'MPO', 'CMA1', 'ORC6', 'GPX3', 'CAPN9', 'IGFBP7', 'EGR3', 'ACE2', 'TM4SF4', 'DPEP1', 'DMKN', 'TRAT1', 'SCG2', 'PDCD1', 'GLCCI1', 'FCER1G', 'TMA7', 'FERMT1', 'NTN4', 'SNX20', 'SNCG', 'HES1', 'LYVE1', 'FCRL1', 'HAVCR2', 'LILRB4', 'GJA5', 'VEGFA', 'NXPH1', 'IGSF6', 'COTL1', 'C15orf48', 'FLT1', 'UCP1', 'AGR3', 'GABRP', 'AR', 'FCN1', 'GREM2', 'GNA11', 'DAPK3', 'ARSG', 'IGLC3', 'HADHB', 'TRIB1', 'BASP1', 'SELENOK', 'FCRLA', 'MS4A2', 'CCL28', 'CCPG1', 'FBN1', 'S100B', 'PAX4', 'CD6', 'GPRC5A', 'GALNT5', 'CLEC9A', 'DERL3', 'TNFRSF9', 'ETS1', 'CEACAM5', 'CKAP4', 'EGFR', 'ERG', 'TMEM52B', 'AIRE', 'MMP11', 'SELL', 'DAPK2', 'POU2AF1', 'PAMR1', 'CD86', 'JAK2', 'CDKN2D', 'CD28', 'AVPR1A', 'RAMP2', 'RETN', 'NRG1', 'CMBL', 'TC2N',
...
LK11', 'HDC', 'S100P', 'SH3YL1', 'MS4A4A', 'CCR2', 'NEUROD1', 'NPDC1', 'SLC2A1', 'MMRN2', 'PDGFRB', 'MUC1', 'PIM1', 'CCR6', 'CHI3L2', 'TOX', 'PLXND1', 'EPHB3', 'SOCS1', 'CCL19', 'SMYD2', 'KLRF1', 'CHP2', 'KRTCAP3', 'CEACAM6', 'OTUD7B', 'RORA', 'CD24', 'DNASE1L3', 'CXCL1', 'CD274', 'TAPBP', 'GYPA', 'PTTG1', 'ANKRD30A', 'G0S2', 'TBX21', 'PDE4C', 'GNPTAB', 'CYP4B1', 'EHF', 'RBFOX3', 'ADAMTS1', 'IRF8', 'ARPC3', 'CD7', 'EPO', 'SST', 'S100A8', 'SMS', 'MMP1', 'LY86', 'LUM', 'ETV1', 'IL2RG', 'IL22', 'SLC26A2', 'LGALSL', 'ELF3', 'ITGB1', 'ASPN', 'KLRC2', 'CFTR', 'REXO4', 'SLC15A2', 'TACSTD2', 'TIFA', 'DLK1', 'SELP', 'AFAP1L2', 'LMCD1', 'HOXD8', 'MYH11', 'KRT14', 'GKN2', 'CCL7', 'PGR', 'UPK1B', 'PTGER4', 'MRC1', 'CAV1', 'FHIT', 'CTSK', 'AEBP1', 'LDHB', 'SMIM14', 'GPC3', 'NOP53', 'TIMP4', 'SEMA3B', 'GPR171', 'CRISPLD2', 'SERPINA3', 'YAF2', 'HINT1', 'ICOSLG', 'BAIAP2L1', 'UBE2C', 'GRB14', 'IGFBP6', 'CLCA1', 'TIGIT', 'PPP1R1A', 'TK1', 'CXCL11', 'DPYSL3', 'TCL1A', 'FCER1A', 'CFC1', 'CD83', 'FKBP11', 'RARRES2', 'CYP1A1', 'TGFBR1', 'RPS4Y1', 'GATA3', 'ANXA13', 'SLPI', 'MAMDC2', 'MS4A6A', 'DSC2', 'GHRL', 'IFNL1', 'NOSTRIN', 'CSF3', 'SLC5A6', 'SPDEF', 'PPP1R12B', 'TNC', 'INS', 'F3', 'CA1', 'KRT6B', 'NPC2', 'SERPINB1', 'PLA2G7', 'FGFR1', 'SEC11C', 'RHOA', 'TPD52', 'BMP5', 'ANGPT2', 'LEPROTL1', 'LY6E', 'VCAN', 'ADH4', 'CEL', 'LYZ', 'CD68', 'CDKN1C', 'BBOX1', 'RAB3B', 'TNFAIP3', 'RAP1GAP', 'CDK12', 'COCH', 'LILRA4', 'CNN3', 'CD44', 'CCDC78', 'LGR5', 'ITK', 'MCF2L', 'CEACAM1', 'LGALS3BP', 'SPARC'}) and 3 missing columns ({'x_coord', 'cell_id', 'y_coord'}).
This happened while the csv dataset builder was generating data using
hf://datasets/SydneyBioX/GHIST-Plus-bundle/evaluation_data/atera/cell_gene_matrix_filtered.csv (at revision 9d8ede79035c172e1ca14cb55692150158295fb8), ['hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/atera/cell_coords.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/atera/cell_gene_matrix_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast2/cell_coords.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast2/cell_gene_matrix_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast2/cell_type_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast5k/cell_coords_histology_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast5k/cell_gene_matrix_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/imputation/figure3_coordinates.csv.gz', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/imputation/figure3_ground_truth.csv.gz']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._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 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
Unnamed: 0: int64
A2M: int64
ABCA8: int64
ABCC8: int64
ABCC11: int64
ACACB: int64
ACE: int64
ACE2: int64
ACKR1: int64
ACTA2: int64
ACTB: int64
ACTG2: int64
ACTN1: int64
ADAM9: int64
ADAM17: int64
ADAM28: int64
ADAMTS1: int64
ADGRE1: int64
ADGRE5: int64
ADGRL4: int64
ADH1B: int64
ADH1C: int64
ADH4: int64
ADIPOQ: int64
ADRA2A: int64
AEBP1: int64
AFAP1L2: int64
AGER: int64
AGR3: int64
AGTR1: int64
AHSP: int64
AIF1: int64
AIRE: int64
AKR1C1: int64
AKR1C3: int64
AKR7A3: int64
AKT1: int64
ALAS2: int64
ALDH1A3: int64
ALDH1B1: int64
ALOX5AP: int64
AMY2A: int64
ANGPT2: int64
ANK2: int64
ANKRD28: int64
ANKRD29: int64
ANKRD30A: int64
ANO7: int64
ANPEP: int64
ANXA1: int64
ANXA3: int64
ANXA13: int64
APC: int64
APCDD1: int64
APOA5: int64
APOB: int64
APOBEC3A: int64
APOBEC3B: int64
APOC1: int64
APOD: int64
APOE: int64
APOLD1: int64
AQP1: int64
AQP2: int64
AQP3: int64
AQP8: int64
AQP9: int64
AR: int64
AREG: int64
ARFGEF3: int64
ARG1: int64
ARHGAP24: int64
ARID1A: int64
ARL14: int64
ARPC3: int64
ARPC5: int64
ARSG: int64
ARX: int64
ASAH1: int64
ASCL1: int64
ASCL2: int64
ASCL3: int64
ASPN: int64
ATM: int64
ATOH1: int64
ATP1B1: int64
ATP5F1B: int64
ATP5MC2: int64
ATP5MD: int64
AVIL: int64
AVPR1A: int64
AZGP1: int64
B3GNT6: int64
BAALC: int64
BACE2: int64
BAIAP2L1: int64
BAMBI: int64
BANK1: int64
BASP1: int64
BATF: int64
BATF3: int64
BBOX1: int64
BCAS1: int64
BCL2: int64
BCL2L11: int64
BEST2: int64
BEST4: int64
BIRC3: int64
BMP4: int64
BMP5: int64
BMX: int64
BRAF: int64
BRCA2: int64
BTF3: int64
B
...
int64
THBS1: int64
THBS2: int64
THY1: int64
TIFA: int64
TIGIT: int64
TIMP3: int64
TIMP4: int64
TK1: int64
TKT: int64
TM4SF4: int64
TM4SF18: int64
TMA7: int64
TMBIM6: int64
TMC5: int64
TMEM52B: int64
TMEM61: int64
TMEM100: int64
TMEM147: int64
TMEM174: int64
TMIGD1: int64
TMPRSS2: int64
TNC: int64
TNF: int64
TNFAIP3: int64
TNFRSF1B: int64
TNFRSF9: int64
TNFRSF13B: int64
TNFRSF13C: int64
TNFRSF17: int64
TNFRSF18: int64
TNFRSF25: int64
TNFSF13B: int64
TNS4: int64
TNXB: int64
TOMM7: int64
TOP2A: int64
TOX: int64
TP53: int64
TP63: int64
TP73: int64
TPD52: int64
TPSAB1: int64
TPSG1: int64
TRAC: int64
TRAF4: int64
TRAPPC3: int64
TRAT1: int64
TRBC1: int64
TRBC2: int64
TRDN: int64
TRDV1: int64
TREM2: int64
TRGV4: int64
TRH: int64
TRIB1: int64
TRPC6: int64
TRPM5: int64
TSPAN8: int64
TSPAN19: int64
TTR: int64
TUBA1A: int64
TUBA1B: int64
TUBA4A: int64
TUBB: int64
TUBB2B: int64
TXLNA: int64
TYMS: int64
TYROBP: int64
UBD: int64
UBE2C: int64
UCN3: int64
UCP1: int64
UGP2: int64
UGT2A3: int64
UGT2B17: int64
UMOD: int64
UPK1B: int64
UPK3B: int64
UQCC2: int64
USP53: int64
VAMP8: int64
VCAN: int64
VEGFA: int64
VEGFC: int64
VOPP1: int64
VPREB3: int64
VSIG4: int64
VSIR: int64
VTN: int64
VWA5A: int64
VWA5B2: int64
VWF: int64
WARS: int64
WFDC2: int64
WFS1: int64
WNT2: int64
WNT5B: int64
WT1: int64
XBP1: int64
XCL2: int64
XCR1: int64
YAF2: int64
ZEB1: int64
ZEB2: int64
ZNF562: int64
ZNF683: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 136663
to
{'cell_id': Value('int64'), 'x_coord': Value('float64'), 'y_coord': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
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 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1276 new columns ({'TRAPPC3', 'MARCO', 'FCER2', 'LRRC15', 'CX3CR1', 'CYTIP', 'TM4SF18', 'SDC4', 'PPP1R1B', 'APOB', 'CES1', 'NOTCH1', 'SLC18A2', 'CA4', 'SERHL2', 'HMGB2', 'STEAP4', 'SSR2', 'ALDH1B1', 'LAMC3', 'SFTA2', 'BATF3', 'FCN2', 'PLCG2', 'SOD3', 'RNASE1', 'SNTN', 'CFHR1', 'POSTN', 'RETNLB', 'FRZB', 'CCL11', 'CTLA4', 'KRT20', 'IL22RA2', 'SFRP2', 'CCDC39', 'CXCR4', 'CD79A', 'SNCA', 'TREM2', 'FBLN2', 'CREB3L1', 'PAPLN', 'C1R', 'PAX5', 'PTPRC', 'E2F1', 'CCL13', 'FGFBP2', 'C11orf96', 'CXCR5', 'IFNAR1', 'KLF6', 'CAVIN1', 'ANXA3', 'SKP1', 'DUOX1', 'MT1A', 'CD93', 'NCAM1', 'CDC42EP1', 'CD96', 'MMP12', 'GPRIN3', 'SSR3', 'COL1A1', 'HLA-B', 'IL4R', 'GPX2', 'CADM2', 'FAM107B', 'ARL14', 'SERPINE1', 'HAMP', 'OLFM4', 'TRDV1', 'LGR6', 'POLR2J3', 'SHANK3', 'MAF', 'CDK1', 'BTNL9', 'CD4', 'CENPF', 'MPO', 'CMA1', 'ORC6', 'GPX3', 'CAPN9', 'IGFBP7', 'EGR3', 'ACE2', 'TM4SF4', 'DPEP1', 'DMKN', 'TRAT1', 'SCG2', 'PDCD1', 'GLCCI1', 'FCER1G', 'TMA7', 'FERMT1', 'NTN4', 'SNX20', 'SNCG', 'HES1', 'LYVE1', 'FCRL1', 'HAVCR2', 'LILRB4', 'GJA5', 'VEGFA', 'NXPH1', 'IGSF6', 'COTL1', 'C15orf48', 'FLT1', 'UCP1', 'AGR3', 'GABRP', 'AR', 'FCN1', 'GREM2', 'GNA11', 'DAPK3', 'ARSG', 'IGLC3', 'HADHB', 'TRIB1', 'BASP1', 'SELENOK', 'FCRLA', 'MS4A2', 'CCL28', 'CCPG1', 'FBN1', 'S100B', 'PAX4', 'CD6', 'GPRC5A', 'GALNT5', 'CLEC9A', 'DERL3', 'TNFRSF9', 'ETS1', 'CEACAM5', 'CKAP4', 'EGFR', 'ERG', 'TMEM52B', 'AIRE', 'MMP11', 'SELL', 'DAPK2', 'POU2AF1', 'PAMR1', 'CD86', 'JAK2', 'CDKN2D', 'CD28', 'AVPR1A', 'RAMP2', 'RETN', 'NRG1', 'CMBL', 'TC2N',
...
LK11', 'HDC', 'S100P', 'SH3YL1', 'MS4A4A', 'CCR2', 'NEUROD1', 'NPDC1', 'SLC2A1', 'MMRN2', 'PDGFRB', 'MUC1', 'PIM1', 'CCR6', 'CHI3L2', 'TOX', 'PLXND1', 'EPHB3', 'SOCS1', 'CCL19', 'SMYD2', 'KLRF1', 'CHP2', 'KRTCAP3', 'CEACAM6', 'OTUD7B', 'RORA', 'CD24', 'DNASE1L3', 'CXCL1', 'CD274', 'TAPBP', 'GYPA', 'PTTG1', 'ANKRD30A', 'G0S2', 'TBX21', 'PDE4C', 'GNPTAB', 'CYP4B1', 'EHF', 'RBFOX3', 'ADAMTS1', 'IRF8', 'ARPC3', 'CD7', 'EPO', 'SST', 'S100A8', 'SMS', 'MMP1', 'LY86', 'LUM', 'ETV1', 'IL2RG', 'IL22', 'SLC26A2', 'LGALSL', 'ELF3', 'ITGB1', 'ASPN', 'KLRC2', 'CFTR', 'REXO4', 'SLC15A2', 'TACSTD2', 'TIFA', 'DLK1', 'SELP', 'AFAP1L2', 'LMCD1', 'HOXD8', 'MYH11', 'KRT14', 'GKN2', 'CCL7', 'PGR', 'UPK1B', 'PTGER4', 'MRC1', 'CAV1', 'FHIT', 'CTSK', 'AEBP1', 'LDHB', 'SMIM14', 'GPC3', 'NOP53', 'TIMP4', 'SEMA3B', 'GPR171', 'CRISPLD2', 'SERPINA3', 'YAF2', 'HINT1', 'ICOSLG', 'BAIAP2L1', 'UBE2C', 'GRB14', 'IGFBP6', 'CLCA1', 'TIGIT', 'PPP1R1A', 'TK1', 'CXCL11', 'DPYSL3', 'TCL1A', 'FCER1A', 'CFC1', 'CD83', 'FKBP11', 'RARRES2', 'CYP1A1', 'TGFBR1', 'RPS4Y1', 'GATA3', 'ANXA13', 'SLPI', 'MAMDC2', 'MS4A6A', 'DSC2', 'GHRL', 'IFNL1', 'NOSTRIN', 'CSF3', 'SLC5A6', 'SPDEF', 'PPP1R12B', 'TNC', 'INS', 'F3', 'CA1', 'KRT6B', 'NPC2', 'SERPINB1', 'PLA2G7', 'FGFR1', 'SEC11C', 'RHOA', 'TPD52', 'BMP5', 'ANGPT2', 'LEPROTL1', 'LY6E', 'VCAN', 'ADH4', 'CEL', 'LYZ', 'CD68', 'CDKN1C', 'BBOX1', 'RAB3B', 'TNFAIP3', 'RAP1GAP', 'CDK12', 'COCH', 'LILRA4', 'CNN3', 'CD44', 'CCDC78', 'LGR5', 'ITK', 'MCF2L', 'CEACAM1', 'LGALS3BP', 'SPARC'}) and 3 missing columns ({'x_coord', 'cell_id', 'y_coord'}).
This happened while the csv dataset builder was generating data using
hf://datasets/SydneyBioX/GHIST-Plus-bundle/evaluation_data/atera/cell_gene_matrix_filtered.csv (at revision 9d8ede79035c172e1ca14cb55692150158295fb8), ['hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/atera/cell_coords.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/atera/cell_gene_matrix_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast2/cell_coords.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast2/cell_gene_matrix_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast2/cell_type_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast5k/cell_coords_histology_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/breast5k/cell_gene_matrix_filtered.csv', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/imputation/figure3_coordinates.csv.gz', 'hf://datasets/SydneyBioX/GHIST-Plus-bundle@9d8ede79035c172e1ca14cb55692150158295fb8/evaluation_data/imputation/figure3_ground_truth.csv.gz']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
cell_id int64 | x_coord float64 | y_coord float64 |
|---|---|---|
1 | 77,496.841733 | 4,082.609656 |
2 | 77,537.900401 | 4,370.606505 |
3 | 77,184.576033 | 3,873.576296 |
4 | 77,250.12938 | 3,869.166524 |
5 | 77,384.880698 | 4,028.793313 |
6 | 77,402.927754 | 4,039.828016 |
7 | 77,608.767233 | 4,698.044269 |
8 | 77,262.205517 | 4,773.734259 |
10 | 77,645.965348 | 4,964.74568 |
11 | 77,504.164625 | 4,436.79834 |
12 | 77,533.368381 | 4,458.082255 |
13 | 77,377.910075 | 4,424.744235 |
14 | 77,405.112541 | 4,455.038862 |
15 | 77,491.16299 | 4,457.368883 |
16 | 77,757.498847 | 4,406.618391 |
17 | 77,681.642409 | 4,687.665622 |
18 | 77,767.367421 | 4,676.995191 |
19 | 77,661.391378 | 4,519.183994 |
20 | 77,675.221637 | 4,090.759088 |
21 | 77,707.766352 | 4,350.739595 |
22 | 77,670.613108 | 4,131.705586 |
23 | 75,576.460471 | 5,207.854871 |
24 | 72,702.227606 | 4,879.310757 |
25 | 71,615.779445 | 4,955.108398 |
27 | 71,881.301518 | 4,835.285078 |
28 | 73,490.032672 | 4,964.085858 |
29 | 72,101.87823 | 4,718.409513 |
30 | 74,586.200639 | 4,941.230864 |
31 | 73,822.263766 | 4,986.84304 |
32 | 73,926.958675 | 5,022.137536 |
33 | 74,305.788181 | 4,863.563015 |
34 | 74,314.140861 | 4,908.361581 |
36 | 72,131.051938 | 5,246.706835 |
37 | 74,577.292972 | 5,265.406334 |
38 | 72,293.407026 | 5,231.636427 |
39 | 73,952.353312 | 5,220.429419 |
40 | 73,816.456741 | 5,189.144562 |
41 | 74,731.157576 | 5,226.575805 |
42 | 74,768.826378 | 5,199.801215 |
43 | 74,902.167273 | 5,206.50993 |
44 | 75,432.08101 | 4,879.138671 |
45 | 75,269.804428 | 4,755.513862 |
47 | 76,934.155068 | 4,929.299145 |
48 | 76,532.563511 | 4,782.766704 |
49 | 76,625.775811 | 4,705.831506 |
50 | 76,925.040744 | 5,072.876723 |
52 | 75,675.618704 | 4,985.018186 |
53 | 75,700.048299 | 4,955.589336 |
54 | 75,679.760741 | 4,838.358601 |
55 | 76,360.769952 | 4,974.524822 |
56 | 76,035.965887 | 5,090.526428 |
57 | 76,264.704636 | 5,153.247145 |
58 | 75,707.956881 | 4,638.28954 |
60 | 75,787.707225 | 4,672.027241 |
63 | 76,622.848298 | 4,638.162573 |
64 | 77,227.710211 | 5,124.79241 |
65 | 77,017.890736 | 5,189.001011 |
66 | 77,140.003196 | 5,199.766397 |
67 | 76,903.310249 | 5,122.009091 |
68 | 77,547.153788 | 5,146.891362 |
69 | 77,254.809826 | 5,044.80904 |
70 | 77,094.931419 | 5,000.251794 |
71 | 77,099.166428 | 4,726.981833 |
72 | 77,181.046944 | 4,752.219104 |
73 | 77,288.648005 | 4,422.325687 |
74 | 77,183.830322 | 4,548.79255 |
75 | 77,316.311911 | 4,502.802414 |
76 | 75,746.846489 | 4,437.075111 |
77 | 74,481.789582 | 4,603.64529 |
78 | 74,740.094629 | 4,578.377749 |
79 | 74,614.797527 | 4,712.509595 |
81 | 75,326.258132 | 4,726.243822 |
82 | 75,203.259187 | 4,636.961879 |
84 | 75,432.077192 | 4,572.807333 |
85 | 75,303.960476 | 4,448.932645 |
88 | 73,385.801245 | 4,437.450125 |
89 | 72,769.643886 | 4,782.034326 |
90 | 73,117.966729 | 4,829.269108 |
91 | 73,161.121075 | 4,594.905998 |
92 | 73,166.042679 | 4,365.272054 |
93 | 75,017.656489 | 4,705.778952 |
94 | 74,830.960171 | 4,440.135673 |
95 | 74,883.415466 | 4,745.827906 |
97 | 76,810.227061 | 4,366.586414 |
98 | 76,753.596723 | 3,984.775452 |
100 | 75,644.886195 | 4,117.98209 |
101 | 74,480.035718 | 4,077.647946 |
106 | 75,086.049028 | 4,287.121509 |
107 | 75,865.171736 | 4,371.789775 |
110 | 76,368.870049 | 4,087.135858 |
113 | 72,251.736973 | 6,483.371723 |
114 | 72,210.426341 | 6,494.434017 |
115 | 72,225.502479 | 6,504.196605 |
116 | 72,223.740039 | 6,471.897379 |
117 | 72,175.801668 | 6,584.235427 |
118 | 71,974.014053 | 6,610.985756 |
119 | 71,974.541898 | 6,591.541713 |
120 | 72,174.0108 | 6,459.324372 |
121 | 71,999.141507 | 6,494.616251 |
122 | 72,190.200386 | 6,505.014207 |
End of preview.
GHIST+ data and model bundle
This bundle contains the model artifacts, predictions, evaluation inputs, comparison outputs, and plot-ready tables released with GHIST+. Source code is provided in that repository.
Download
hf download SydneyBioX/GHIST-Plus-bundle \
--repo-type dataset \
--local-dir bundle
The bundle is approximately 38 GB. Individual files can also be downloaded from this page.
Use with the figure notebooks
These notebooks regenerate figures from released expression predictions and analysis tables. Training and checkpoint reconstruction are not required.
Complete the GitHub installation and notebook setup, then run:
conda activate model_env
cd GHIST_plus
export GHIST_BUNDLE_ROOT="/path/to/bundle"
python -m jupyterlab
Set GHIST_BUNDLE_ROOT to the downloaded bundle directory containing
GHIST_plus/, evaluation_data/, figure_data/, and other_models/.
Open the desired Figure3.ipynb–Figure5.ipynb, select GHIST+ (model_env),
and choose Restart Kernel and Run All. Figures appear inside the notebook;
save the notebook to keep its outputs.
Model checkpoints
The four released GHIST+ checkpoint files are:
- GHIST_plus/models/breast_multi/ghist_plus_breast_multi_checkpoint.pth
- GHIST_plus/models/breast_single/ghist_plus_breast_single_checkpoint.pth
- GHIST_plus/models/imputation/ghist_plus_gene_imputation_checkpoint.pth
- GHIST_plus/models/pancancer/ghist_plus_pancancer_checkpoint.pth
They exclude the frozen third-party UNI2-H encoder weights. Users must obtain UNI2-H directly from MahmoodLab/UNI2-h, accept its terms, and follow the Pretrained Checkpoints instructions in the GHIST+ README. That procedure reconstructs the complete checkpoints locally at the same filenames, so the existing inference commands and configs remain unchanged.
The bundle does not redistribute UNI2-H weights.
Contents
Figure 3: evaluation data, GHIST+ predictions, and comparison-model predictions under
evaluation_data/,GHIST_plus/predictions/, andother_models/.Figure 4: imputation inputs and predictions, including the bundled VQ/composition ablation predictions.
Figure 5: paired PCC and coverage tables under
figure_data/figure5/.bundle/ ├── GHIST_plus/ │ ├── models/ │ └── predictions/ ├── evaluation_data/ ├── figure_data/ └── other_models/
The paths and lightweight input schemas were checked against the released Figure 3–5 notebooks.
Data and third-party terms
The bundle combines author-generated artifacts, processed public source data, and outputs from comparison methods. It therefore has no single blanket license; the Hugging Face license field is other. See THIRD_PARTY_NOTICES.md for component-specific sources, versions, attributions, and terms. Upstream terms remain applicable.
Tutorial data
tutorial.ipynb does not use this bundle as its DATA_ROOT. The tutorial requires a separately prepared GHIST data directory containing aligned H&E images, segmentation masks, nuclei metadata, and inputs for the selected training mode.
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