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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 1306 new columns ({'MB-2778', 'MB-4012', 'MB-0120', 'MB-0451', 'MB-4666', 'MB-4578', 'MB-7081', 'MB-6359', 'MB-0198', 'MB-6178', 'MB-4785', 'MB-4626', 'MB-0167', 'MB-7295', 'MB-0249', 'MB-4737', 'MB-0519', 'MB-4692', 'MB-4784', 'MB-2845', 'MB-7185', 'MB-5011', 'MB-5043', 'MB-4749', 'MB-4925', 'MB-4982', 'MB-0487', 'MB-5591', 'MB-3500', 'MB-6085', 'MB-0481', 'MB-0600', 'MB-4881', 'MB-0553', 'MB-5308', 'MB-4416', 'MB-0891', 'MB-0436', 'MB-4119', 'MB-4024', 'MB-7056', 'MB-0438', 'MB-7090', 'MB-0093', 'MB-0284', 'MB-0315', 'MB-5004', 'MB-0202', 'MB-6263', 'MB-5139', 'MB-2629', 'MB-5567', 'MB-4965', 'MB-4707', 'MB-7154', 'MB-4871', 'MB-6184', 'MB-4938', 'MB-7141', 'MB-5226', 'MB-4716', 'MB-5431', 'MB-5347', 'MB-7189', 'MB-3083', 'MB-0142', 'MB-4735', 'MB-0615', 'MB-5455', 'MB-4353', 'MB-7200', 'MB-4708', 'MB-5450', 'MB-5124', 'MB-5412', 'MB-4858', 'MB-5475', 'MB-4643', 'MB-4618', 'MB-5072', 'MB-5163', 'MB-5414', 'MB-2669', 'MB-0270', 'MB-0215', 'MB-5559', 'MB-0526', 'MB-0166', 'MB-5397', 'MB-0660', 'MB-4332', 'MB-6024', 'MB-0305', 'MB-7269', 'MB-4434', 'MB-0134', 'MB-3110', 'MB-6036', 'MB-4710', 'MB-5130', 'MB-7228', 'MB-5267', 'MB-4764', 'MB-4717', 'MB-5425', 'MB-5299', 'MB-2643', 'MB-3567', 'MB-4654', 'MB-2686', 'MB-4968', 'MB-0201', 'MB-7218', 'MB-7130', 'MB-3711', 'MB-7065', 'MB-0162', 'MB-0605', 'MB-0178', 'MB-0410', 'MB-5312', 'MB-5322', 'MB-3088', 'MB-4904', 'MB-0356', 'MB-5013', 'MB-6188', 'MB-3360', 'MB-5565', 'MB-2763', 'MB-5277', 'MB-2919', 'MB-7287', 'MB-3389', 'MB-7054', 'MB-4672', 'MB
...
14', 'MB-4763', 'MB-5470', 'MB-6208', 'MB-5389', 'MB-2952', 'MB-5603', 'MB-5238', 'MB-5395', 'MB-0322', 'MB-6287', 'MB-5498', 'MB-4599', 'MB-5065', 'MB-6160', 'MB-0152', 'MB-3383', 'MB-0155', 'MB-3528', 'MB-0580', 'MB-7292', 'MB-2618', 'MB-7075', 'MB-7072', 'MB-6363', 'MB-5576', 'MB-5497', 'MB-2771', 'MB-0641', 'MB-4098', 'MB-3412', 'MB-6239', 'MB-0303', 'MB-3502', 'MB-3378', 'MB-0174', 'MB-0308', 'MB-7083', 'MB-3490', 'MB-2970', 'MB-5331', 'MB-0221', 'MB-0181', 'MB-5193', 'MB-5101', 'MB-4976', 'MB-0364', 'MB-7014', 'MB-2969', 'MB-7000', 'MB-0368', 'MB-2711', 'MB-5365', 'MB-7104', 'MB-5066', 'MB-4791', 'MB-0434', 'MB-4005', 'MB-0097', 'MB-0506', 'MB-6281', 'MB-2645', 'MB-0584', 'MB-0570', 'MB-5655', 'MB-0482', 'MB-5239', 'MB-3497', 'MB-7032', 'MB-2730', 'MB-3008', 'MB-0222', 'MB-7004', 'MB-0630', 'MB-3510', 'MB-5553', 'MB-7045', 'MB-6092', 'MB-3417', 'MB-5589', 'MB-3298', 'MB-5114', 'MB-7170', 'MB-3871', 'MB-5058', 'MB-5054', 'MB-4870', 'MB-5653', 'MB-7258', 'MB-4145', 'MB-2931', 'MB-2895', 'MB-3046', 'MB-5300', 'MB-0372', 'MB-0151', 'MB-4328', 'MB-5457', 'MB-7066', 'MB-7144', 'MB-4281', 'MB-3344', 'MB-6144', 'MB-2858', 'MB-4801', 'MB-2977', 'MB-4324', 'MB-0203', 'MB-2753', 'MB-5390', 'MB-4897', 'MB-5126', 'MB-7127', 'MB-0121', 'MB-6105', 'MB-5543', 'MB-2960', 'MB-3063', 'MB-3165', 'MB-7226', 'MB-5053', 'MB-4935', 'MB-4839', 'MB-7205', 'MB-4743', 'MB-0466', 'MB-0453', 'MB-4996', 'MB-6211', 'MB-7232', 'MB-6083', 'MB-4141', 'MB-3211', 'MB-0496', 'MB-7244', 'MB-7060', 'MB-5388'}) and 23 missing columns ({'HISTOLOGICAL_SUBTYPE', 'OS_STATUS', 'BREAST_SURGERY', 'VITAL_STATUS', 'INTCLUST', 'AGE_AT_DIAGNOSIS', 'ER_IHC', 'RFS_MONTHS', 'CHEMOTHERAPY', 'INFERRED_MENOPAUSAL_STATE', 'HORMONE_THERAPY', 'NPI', 'RFS_STATUS', 'CLAUDIN_SUBTYPE', 'THREEGENE', 'LATERALITY', 'OS_MONTHS', 'RADIO_THERAPY', 'HER2_SNP6', 'COHORT', 'SEX', 'CELLULARITY', 'LYMPH_NODES_EXAMINED_POSITIVE'}).

This happened while the csv dataset builder was generating data using

hf://datasets/huseyincavus/flexynesis-datasets/brca_metabric_processed/train/cna.csv (at revision 69b9b84b0f230dd3a6c8d347599ca8470ac82ed3), [/tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/cna.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/cna.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/gex.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/gex.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/mut.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/mut.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/cnv.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/cnv.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/gex.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/gex.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/gex.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/gex.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/meth.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/meth.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/cna.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/cna.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/mut.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/mut.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/ADT.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/ADT.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/RNA.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/RNA.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/clin.csv)]

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/python3.12/site-packages/datasets/builder.py", line 1800, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              MB-2960: int64
              MB-0511: int64
              MB-5204: int64
              MB-5074: int64
              MB-5322: int64
              MB-5193: int64
              MB-3277: int64
              MB-0134: int64
              MB-5171: int64
              MB-5434: int64
              MB-0097: int64
              MB-0571: int64
              MB-6337: int64
              MB-0303: int64
              MB-3211: int64
              MB-6185: int64
              MB-0641: int64
              MB-4710: int64
              MB-5427: int64
              MB-3063: int64
              MB-5408: int64
              MB-4758: int64
              MB-4881: int64
              MB-3181: int64
              MB-6156: int64
              MB-5294: int64
              MB-4651: int64
              MB-0426: int64
              MB-5453: int64
              MB-2750: int64
              MB-5428: int64
              MB-7288: int64
              MB-5211: int64
              MB-0482: int64
              MB-0637: int64
              MB-0501: int64
              MB-7044: int64
              MB-5421: int64
              MB-4598: int64
              MB-4859: int64
              MB-0269: int64
              MB-7014: int64
              MB-5505: int64
              MB-0350: int64
              MB-6154: int64
              MB-0383: int64
              MB-2795: int64
              MB-5520: int64
              MB-4862: int64
              MB-3871: int64
              MB-4785: int64
              MB-5347: int64
              MB-4741: int64
              MB-3235: int64
              MB-0429: int64
              MB-0611: int64
              MB-0245: int64
              MB-5549: int64
              MB-4944: int64
              MB-0590: int64
              MB-5599: int64
              MB-4235: int64
              MB-4925: int64
              MB-3530: int64
              MB-3360: int64
              MB-0121: int64
              MB-2834: int64
              MB-0112: int64
              MB-7232: int64
              MB-0207: int64
              MB-7295: int64
              MB-0221: int64
              MB-4888: int64
              MB-5475: int64
              MB-7067: int64
              MB-0172: int64
              MB-4855: int64
              MB-6101: int64
              MB-6214: int64
              MB-4004: int64
              MB-0062: int64
              MB-5525: int64
              MB-7143: int64
              MB-5351: int64
              MB-4897: int64
              MB-5471: int64
              MB-4869: int64
              MB-7025: int64
              MB-7038: int64
              MB-0178: int64
              MB-4730: int64
              MB-0516: int64
              MB-7015: int64
              MB-5529: int64
              MB-4794: int64
              MB-0479: int64
              MB-0273: int64
              MB-4880: int64
              MB-6254: int64
              MB-0204: int64
              
              ...
              
              MB-5425: int64
              MB-7205: int64
              MB-4024: int64
              MB-7087: int64
              MB-5260: int64
              MB-5431: int64
              MB-7231: int64
              MB-5004: int64
              MB-7216: int64
              MB-0386: int64
              MB-0352: int64
              MB-0626: int64
              MB-0144: int64
              MB-0580: int64
              MB-5646: int64
              MB-4757: int64
              MB-4822: int64
              MB-4739: int64
              MB-6141: int64
              MB-6302: int64
              MB-5298: int64
              MB-7176: int64
              MB-0398: int64
              MB-0180: int64
              MB-3060: int64
              MB-5417: double
              MB-7062: int64
              MB-2827: int64
              MB-3506: int64
              MB-5583: int64
              MB-0453: int64
              MB-4935: int64
              MB-3396: int64
              MB-5323: int64
              MB-4784: int64
              MB-5530: int64
              MB-7165: int64
              MB-5223: int64
              MB-4705: int64
              MB-2953: int64
              MB-6329: double
              MB-2815: int64
              MB-7285: int64
              MB-2626: int64
              MB-4993: int64
              MB-7275: int64
              MB-5189: int64
              MB-0262: int64
              MB-4886: int64
              MB-5123: int64
              MB-5441: int64
              MB-5450: int64
              MB-2999: int64
              MB-7100: int64
              MB-4331: int64
              MB-4353: int64
              MB-5238: int64
              MB-3865: int64
              MB-3435: int64
              MB-3378: int64
              MB-5513: int64
              MB-4300: int64
              MB-5118: int64
              MB-0583: int64
              MB-0558: int64
              MB-0400: int64
              MB-6131: int64
              MB-0109: int64
              MB-4928: int64
              MB-0365: int64
              MB-0008: double
              MB-0640: int64
              MB-7283: int64
              MB-0353: int64
              MB-7266: int64
              MB-2747: int64
              MB-5470: int64
              MB-6189: int64
              MB-5559: int64
              MB-5575: int64
              MB-0579: int64
              MB-4899: int64
              MB-0584: int64
              MB-0361: int64
              MB-4801: int64
              MB-4670: int64
              MB-5365: int64
              MB-4283: int64
              MB-6082: int64
              MB-5073: int64
              MB-0657: int64
              __index_level_0__: string
              -- schema metadata --
              pandas: '{"index_columns": ["__index_level_0__"], "column_indexes": [{"na' + 145322
              to
              {'LYMPH_NODES_EXAMINED_POSITIVE': Value('int64'), 'NPI': Value('float64'), 'CELLULARITY': Value('string'), 'CHEMOTHERAPY': Value('string'), 'COHORT': Value('string'), 'ER_IHC': Value('string'), 'HER2_SNP6': Value('string'), 'HORMONE_THERAPY': Value('string'), 'INFERRED_MENOPAUSAL_STATE': Value('string'), 'SEX': Value('string'), 'INTCLUST': Value('string'), 'AGE_AT_DIAGNOSIS': Value('float64'), 'OS_MONTHS': Value('float64'), 'OS_STATUS': Value('string'), 'CLAUDIN_SUBTYPE': Value('string'), 'THREEGENE': Value('string'), 'VITAL_STATUS': Value('string'), 'LATERALITY': Value('string'), 'RADIO_THERAPY': Value('string'), 'HISTOLOGICAL_SUBTYPE': Value('string'), 'BREAST_SURGERY': Value('string'), 'RFS_STATUS': Value('string'), 'RFS_MONTHS': Value('float64'), '__index_level_0__': Value('string')}
              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 1348, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1802, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              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 1306 new columns ({'MB-2778', 'MB-4012', 'MB-0120', 'MB-0451', 'MB-4666', 'MB-4578', 'MB-7081', 'MB-6359', 'MB-0198', 'MB-6178', 'MB-4785', 'MB-4626', 'MB-0167', 'MB-7295', 'MB-0249', 'MB-4737', 'MB-0519', 'MB-4692', 'MB-4784', 'MB-2845', 'MB-7185', 'MB-5011', 'MB-5043', 'MB-4749', 'MB-4925', 'MB-4982', 'MB-0487', 'MB-5591', 'MB-3500', 'MB-6085', 'MB-0481', 'MB-0600', 'MB-4881', 'MB-0553', 'MB-5308', 'MB-4416', 'MB-0891', 'MB-0436', 'MB-4119', 'MB-4024', 'MB-7056', 'MB-0438', 'MB-7090', 'MB-0093', 'MB-0284', 'MB-0315', 'MB-5004', 'MB-0202', 'MB-6263', 'MB-5139', 'MB-2629', 'MB-5567', 'MB-4965', 'MB-4707', 'MB-7154', 'MB-4871', 'MB-6184', 'MB-4938', 'MB-7141', 'MB-5226', 'MB-4716', 'MB-5431', 'MB-5347', 'MB-7189', 'MB-3083', 'MB-0142', 'MB-4735', 'MB-0615', 'MB-5455', 'MB-4353', 'MB-7200', 'MB-4708', 'MB-5450', 'MB-5124', 'MB-5412', 'MB-4858', 'MB-5475', 'MB-4643', 'MB-4618', 'MB-5072', 'MB-5163', 'MB-5414', 'MB-2669', 'MB-0270', 'MB-0215', 'MB-5559', 'MB-0526', 'MB-0166', 'MB-5397', 'MB-0660', 'MB-4332', 'MB-6024', 'MB-0305', 'MB-7269', 'MB-4434', 'MB-0134', 'MB-3110', 'MB-6036', 'MB-4710', 'MB-5130', 'MB-7228', 'MB-5267', 'MB-4764', 'MB-4717', 'MB-5425', 'MB-5299', 'MB-2643', 'MB-3567', 'MB-4654', 'MB-2686', 'MB-4968', 'MB-0201', 'MB-7218', 'MB-7130', 'MB-3711', 'MB-7065', 'MB-0162', 'MB-0605', 'MB-0178', 'MB-0410', 'MB-5312', 'MB-5322', 'MB-3088', 'MB-4904', 'MB-0356', 'MB-5013', 'MB-6188', 'MB-3360', 'MB-5565', 'MB-2763', 'MB-5277', 'MB-2919', 'MB-7287', 'MB-3389', 'MB-7054', 'MB-4672', 'MB
              ...
              14', 'MB-4763', 'MB-5470', 'MB-6208', 'MB-5389', 'MB-2952', 'MB-5603', 'MB-5238', 'MB-5395', 'MB-0322', 'MB-6287', 'MB-5498', 'MB-4599', 'MB-5065', 'MB-6160', 'MB-0152', 'MB-3383', 'MB-0155', 'MB-3528', 'MB-0580', 'MB-7292', 'MB-2618', 'MB-7075', 'MB-7072', 'MB-6363', 'MB-5576', 'MB-5497', 'MB-2771', 'MB-0641', 'MB-4098', 'MB-3412', 'MB-6239', 'MB-0303', 'MB-3502', 'MB-3378', 'MB-0174', 'MB-0308', 'MB-7083', 'MB-3490', 'MB-2970', 'MB-5331', 'MB-0221', 'MB-0181', 'MB-5193', 'MB-5101', 'MB-4976', 'MB-0364', 'MB-7014', 'MB-2969', 'MB-7000', 'MB-0368', 'MB-2711', 'MB-5365', 'MB-7104', 'MB-5066', 'MB-4791', 'MB-0434', 'MB-4005', 'MB-0097', 'MB-0506', 'MB-6281', 'MB-2645', 'MB-0584', 'MB-0570', 'MB-5655', 'MB-0482', 'MB-5239', 'MB-3497', 'MB-7032', 'MB-2730', 'MB-3008', 'MB-0222', 'MB-7004', 'MB-0630', 'MB-3510', 'MB-5553', 'MB-7045', 'MB-6092', 'MB-3417', 'MB-5589', 'MB-3298', 'MB-5114', 'MB-7170', 'MB-3871', 'MB-5058', 'MB-5054', 'MB-4870', 'MB-5653', 'MB-7258', 'MB-4145', 'MB-2931', 'MB-2895', 'MB-3046', 'MB-5300', 'MB-0372', 'MB-0151', 'MB-4328', 'MB-5457', 'MB-7066', 'MB-7144', 'MB-4281', 'MB-3344', 'MB-6144', 'MB-2858', 'MB-4801', 'MB-2977', 'MB-4324', 'MB-0203', 'MB-2753', 'MB-5390', 'MB-4897', 'MB-5126', 'MB-7127', 'MB-0121', 'MB-6105', 'MB-5543', 'MB-2960', 'MB-3063', 'MB-3165', 'MB-7226', 'MB-5053', 'MB-4935', 'MB-4839', 'MB-7205', 'MB-4743', 'MB-0466', 'MB-0453', 'MB-4996', 'MB-6211', 'MB-7232', 'MB-6083', 'MB-4141', 'MB-3211', 'MB-0496', 'MB-7244', 'MB-7060', 'MB-5388'}) and 23 missing columns ({'HISTOLOGICAL_SUBTYPE', 'OS_STATUS', 'BREAST_SURGERY', 'VITAL_STATUS', 'INTCLUST', 'AGE_AT_DIAGNOSIS', 'ER_IHC', 'RFS_MONTHS', 'CHEMOTHERAPY', 'INFERRED_MENOPAUSAL_STATE', 'HORMONE_THERAPY', 'NPI', 'RFS_STATUS', 'CLAUDIN_SUBTYPE', 'THREEGENE', 'LATERALITY', 'OS_MONTHS', 'RADIO_THERAPY', 'HER2_SNP6', 'COHORT', 'SEX', 'CELLULARITY', 'LYMPH_NODES_EXAMINED_POSITIVE'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/huseyincavus/flexynesis-datasets/brca_metabric_processed/train/cna.csv (at revision 69b9b84b0f230dd3a6c8d347599ca8470ac82ed3), [/tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/cna.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/cna.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/gex.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/gex.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/mut.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/brca_metabric_processed/train/mut.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/cnv.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/cnv.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/gex.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset1/train/gex.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/gex.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/gex.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/meth.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/dataset2/train/meth.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/clin.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/cna.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/cna.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/mut.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/lgggbm_tcga_pub_processed/train/mut.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/ADT.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/ADT.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/RNA.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/RNA.csv), /tmp/hf-datasets-cache/medium/datasets/92917345236671-config-parquet-and-info-huseyincavus-flexynesis-d-d8c38db6/hub/datasets--huseyincavus--flexynesis-datasets/snapshots/69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/clin.csv (origin=hf://datasets/huseyincavus/flexynesis-datasets@69b9b84b0f230dd3a6c8d347599ca8470ac82ed3/singlecell_bonemarrow/train/clin.csv)]
              
              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.

LYMPH_NODES_EXAMINED_POSITIVE
int64
NPI
float64
CELLULARITY
string
CHEMOTHERAPY
string
COHORT
string
ER_IHC
string
HER2_SNP6
string
HORMONE_THERAPY
string
INFERRED_MENOPAUSAL_STATE
string
SEX
string
INTCLUST
string
AGE_AT_DIAGNOSIS
float64
OS_MONTHS
float64
OS_STATUS
string
CLAUDIN_SUBTYPE
string
THREEGENE
string
VITAL_STATUS
string
LATERALITY
string
RADIO_THERAPY
string
HISTOLOGICAL_SUBTYPE
string
BREAST_SURGERY
string
RFS_STATUS
string
RFS_MONTHS
float64
__index_level_0__
string
0
4.05
Moderate
NO
cohort2
Positve
NEUTRAL
YES
Post
Female
6
69.67
222.2
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
219.28
MB-2960
0
3.062
Moderate
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
3
59.84
61.7
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Left
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
60.89
MB-0511
3
2.048
Moderate
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
3
76.91
191.933333
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Right
NO
Ductal/NST
BREAST CONSERVING
1:Recurred
158.06
MB-5204
0
4.05
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
3
70.59
221.6
1:DECEASED
LumA
ER+/HER2- High Prolif
Died of Other Causes
Left
YES
Lobular
BREAST CONSERVING
1:Recurred
161.97
MB-5074
1
4.046
Moderate
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
2
66.58
102.3
1:DECEASED
LumA
ER+/HER2- High Prolif
Died of Disease
Right
YES
Mixed
BREAST CONSERVING
1:Recurred
66.55
MB-5322
1
3.06
Moderate
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
7
88.8
54.266667
1:DECEASED
LumA
ER+/HER2- High Prolif
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
53.55
MB-5193
3
5.06
High
YES
cohort2
Negative
NEUTRAL
NO
Pre
Female
1
29.92
32.933333
1:DECEASED
Basal
ER-/HER2-
Died of Disease
Left
YES
Ductal/NST
MASTECTOMY
1:Recurred
22.11
MB-3277
3
5.044
High
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
8
73.11
12.933333
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
Left
YES
Ductal/NST
MASTECTOMY
1:Recurred
11.71
MB-0134
0
3.022
Low
NO
cohort3
Positve
NEUTRAL
NO
Pre
Female
4ER+
46.79
185
1:DECEASED
Normal
ER+/HER2- Low Prolif
Died of Disease
Right
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
126.02
MB-5171
1
5.11
Low
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
9
71.07
45.166667
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
Left
NO
Ductal/NST
MASTECTOMY
1:Recurred
37.53
MB-5434
3
5.06
High
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
8
78.19
98.7
0:LIVING
LumA
null
Living
Left
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
97.4
MB-0097
0
2.04
Moderate
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
7
61.79
149.866667
0:LIVING
LumA
ER+/HER2- High Prolif
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
147.89
MB-0571
14
6.11
High
YES
cohort5
Negative
GAIN
NO
Post
Female
5
58.25
110.866667
1:DECEASED
Basal
HER2+
Died of Other Causes
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
109.41
MB-6337
0
4.022
Low
NO
cohort1
Negative
NEUTRAL
YES
Pre
Female
10
47.71
60.133333
0:LIVING
claudin-low
ER-/HER2-
Living
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
59.34
MB-0303
0
4.026
High
NO
cohort2
Negative
NEUTRAL
YES
Post
Female
10
58.31
145.5
0:LIVING
Basal
ER-/HER2-
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
143.59
MB-3211
0
2.032
Low
NO
cohort5
Positve
NEUTRAL
NO
Post
Female
3
75.44
197.733333
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Right
NO
Mixed
MASTECTOMY
0:Not Recurred
195.13
MB-6185
1
4.028
Moderate
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
4ER+
56.34
102.566667
0:LIVING
claudin-low
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
101.22
MB-0641
0
4.038
Moderate
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
3
77.12
57.666667
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
56.91
MB-4710
0
3.05
Moderate
NO
cohort3
Negative
NEUTRAL
YES
Post
Female
9
83.68
155.733333
1:DECEASED
Basal
ER-/HER2-
Died of Other Causes
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
153.68
MB-5427
6
6.04
High
YES
cohort2
Negative
NEUTRAL
NO
Pre
Female
10
43.51
28.566667
1:DECEASED
Basal
ER-/HER2-
Died of Disease
Right
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
20.95
MB-3063
0
3.032
Moderate
NO
cohort3
Positve
GAIN
NO
Post
Female
4ER+
65.48
101.4
0:LIVING
claudin-low
ER-/HER2-
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
100.07
MB-5408
0
4.02
Moderate
NO
cohort3
Negative
NEUTRAL
NO
Pre
Female
10
45.77
211.933333
0:LIVING
Basal
ER-/HER2-
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
209.14
MB-4758
0
4.07
High
NO
cohort3
Negative
NEUTRAL
NO
Pre
Female
10
37.05
274.2
0:LIVING
Basal
ER-/HER2-
Living
Left
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
270.59
MB-4881
0
4.028
High
NO
cohort2
Positve
NEUTRAL
YES
Pre
Female
2
43.6
178.166667
1:DECEASED
LumB
null
Died of Disease
Left
YES
Ductal/NST
MASTECTOMY
1:Recurred
48.29
MB-3181
0
4.05
High
NO
cohort5
Positve
GAIN
NO
Pre
Female
5
49.5
58.933333
1:DECEASED
Her2
HER2+
Died of Disease
Left
NO
Ductal/NST
MASTECTOMY
1:Recurred
28.75
MB-6156
14
6.198
Moderate
YES
cohort3
Negative
NEUTRAL
YES
Pre
Female
10
31.71
195.933333
0:LIVING
Basal
ER-/HER2-
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
193.36
MB-5294
0
3.07
High
NO
cohort3
Positve
NEUTRAL
NO
Post
Female
6
69.96
30.3
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
17.86
MB-4651
2
4.084
Moderate
YES
cohort1
Positve
NEUTRAL
YES
Post
Female
3
50.48
131.1
0:LIVING
Normal
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
129.38
MB-0426
3
5.06
Low
YES
cohort3
Negative
NEUTRAL
NO
Pre
Female
4ER-
36.99
57.3
1:DECEASED
Normal
ER-/HER2-
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
46.28
MB-5453
0
2.06
Moderate
NO
cohort2
Positve
GAIN
NO
Pre
Female
8
47.48
145.433333
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Right
NO
Mixed
MASTECTOMY
1:Recurred
55.26
MB-2750
0
3.03
Low
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
4ER+
55.36
150.466667
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Left
YES
Mixed
BREAST CONSERVING
0:Not Recurred
148.49
MB-5428
8
5.06
High
NO
cohort4
Positve
NEUTRAL
YES
Post
Female
8
55.7
27.066667
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
null
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
9.34
MB-7288
0
4.06
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
7
68.42
247.833333
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Other Causes
Right
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
244.57
MB-5211
1
5.032
Moderate
YES
cohort1
Negative
GAIN
NO
Pre
Female
5
39.53
24.866667
1:DECEASED
Normal
HER2+
Died of Disease
Right
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
15.66
MB-0482
0
4.062
High
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
10
75.71
80.666667
0:LIVING
LumB
ER+/HER2- High Prolif
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
79.61
MB-0637
0
4.042
Moderate
YES
cohort1
Positve
GAIN
YES
Pre
Female
8
48.93
71.5
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
70.56
MB-0501
0
3.03
Low
NO
cohort4
Positve
NEUTRAL
YES
Post
Female
4ER+
67.24
86.833333
0:LIVING
claudin-low
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
85.69
MB-7044
0
4.04
High
NO
cohort3
Negative
NEUTRAL
NO
Post
Female
10
68
194.566667
0:LIVING
Basal
ER-/HER2-
Living
Left
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
192.01
MB-5421
6
5.05
Moderate
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
8
67.92
119.366667
1:DECEASED
LumB
null
Died of Disease
Right
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
62.53
MB-4598
0
3.034
Low
NO
cohort3
Positve
NEUTRAL
NO
Post
Female
4ER+
74.29
157.8
1:DECEASED
claudin-low
ER-/HER2-
Died of Other Causes
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
155.72
MB-4859
2
5.052
Low
YES
cohort1
Negative
NEUTRAL
NO
Pre
Female
4ER-
45.39
22.233333
0:LIVING
claudin-low
ER-/HER2-
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
21.94
MB-0269
1
5.038
High
NO
cohort4
Positve
NEUTRAL
YES
Post
Female
1
67.57
68.7
0:LIVING
Basal
ER+/HER2- High Prolif
Living
Left
YES
Mixed
BREAST CONSERVING
0:Not Recurred
67.8
MB-7014
0
3.03
Moderate
NO
cohort3
Positve
NEUTRAL
NO
Post
Female
3
74.26
164.6
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
Left
YES
Mixed
BREAST CONSERVING
1:Recurred
33.68
MB-5505
0
5.16
High
NO
cohort1
Negative
NEUTRAL
NO
Pre
Female
10
49.05
46.066667
1:DECEASED
Basal
null
Died of Disease
null
NO
Ductal/NST
null
1:Recurred
45.46
MB-0350
0
3.05
Moderate
NO
cohort5
Positve
NEUTRAL
NO
Post
Female
8
71.74
195.3
0:LIVING
LumB
ER+/HER2- High Prolif
Living
Right
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
192.73
MB-6154
0
4.038
High
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
1
65.02
85.733333
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
Left
NO
Ductal/NST
MASTECTOMY
1:Recurred
84.61
MB-0383
0
3.024
Moderate
NO
cohort2
Positve
NEUTRAL
NO
Pre
Female
1
42.07
272.1
0:LIVING
claudin-low
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
268.52
MB-2795
4
6.05
High
NO
cohort3
Positve
NEUTRAL
YES
Pre
Female
7
38.49
16.3
1:DECEASED
LumA
ER+/HER2- High Prolif
Died of Disease
Left
NO
Mixed
MASTECTOMY
1:Recurred
0.82
MB-5520
0
4.046
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
1
64.32
187.3
0:LIVING
LumB
ER+/HER2- High Prolif
Living
Left
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
184.84
MB-4862
1
4.052
High
NO
cohort2
Positve
NEUTRAL
YES
Post
Female
8
62.03
172.8
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
170.53
MB-3871
0
3.056
High
NO
cohort3
Positve
LOSS
NO
Post
Female
8
75.46
121.666667
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
64.47
MB-4785
1
5.05
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
3
56.89
211.9
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
167.01
MB-5347
1
4.04
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
8
53.02
56.5
1:DECEASED
LumB
ER+/HER2- Low Prolif
Died of Disease
Left
NO
Lobular
MASTECTOMY
1:Recurred
21.91
MB-4741
0
4.034
Moderate
NO
cohort2
Positve
GAIN
YES
Post
Female
5
50.66
236.133333
0:LIVING
LumB
ER+/HER2- High Prolif
Living
Right
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
149.21
MB-3235
4
6.08
High
NO
cohort1
Positve
GAIN
YES
Post
Female
1
78.86
74.466667
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Other Causes
Right
YES
Lobular
MASTECTOMY
0:Not Recurred
73.49
MB-0429
0
4.048
Low
NO
cohort1
Positve
GAIN
YES
Post
Female
9
75.65
104.533333
0:LIVING
LumB
ER+/HER2- High Prolif
Living
Right
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
103.16
MB-0611
0
1.028
High
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
3
54.76
164.7
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Right
NO
Lobular
BREAST CONSERVING
0:Not Recurred
162.53
MB-0245
2
4.04
Moderate
YES
cohort3
Negative
GAIN
NO
Post
Female
1
54.83
88.933333
1:DECEASED
Her2
ER-/HER2-
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
67.93
MB-5549
0
3.036
High
NO
cohort3
Positve
NEUTRAL
NO
Post
Female
2
77.77
237.266667
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Other Causes
Left
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
234.14
MB-4944
15
6.06
High
YES
cohort1
Positve
GAIN
YES
Post
Female
9
60.27
72.466667
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Other Causes
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
71.51
MB-0590
3
4.04
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
7
62.88
224.866667
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
221.91
MB-5599
0
3.04
Moderate
NO
cohort3
Positve
NEUTRAL
NO
Post
Female
10
67.46
335.733333
1:DECEASED
Basal
null
Died of Disease
Right
NO
Ductal/NST
MASTECTOMY
1:Recurred
290.89
MB-4235
1
4.05
Moderate
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
7
76.54
102.766667
1:DECEASED
LumB
null
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
101.41
MB-4925
1
4.052
null
NO
cohort2
Positve
NEUTRAL
YES
Post
Female
8
70.94
112.966667
1:DECEASED
Her2
ER+/HER2- High Prolif
Died of Other Causes
Left
NO
Mixed
MASTECTOMY
0:Not Recurred
111.48
MB-3530
0
4.056
High
NO
cohort2
Positve
GAIN
YES
Pre
Female
5
48.48
26.766667
1:DECEASED
LumB
HER2+
Died of Disease
Right
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
25.07
MB-3360
6
5.06
Moderate
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
8
78.73
152.2
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Left
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
150.2
MB-0121
1
4.018
High
NO
cohort2
Positve
NEUTRAL
NO
Post
Female
3
56.85
153.833333
1:DECEASED
Her2
ER-/HER2-
Died of Other Causes
Left
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
151.81
MB-2834
14
6.3
High
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
3
83.89
39.166667
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Disease
Right
YES
Lobular
MASTECTOMY
1:Recurred
25.13
MB-0112
0
3.00424
High
NO
cohort4
Positve
NEUTRAL
NO
Post
Female
8
58.37
177.6
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Right
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
175.26
MB-7232
2
4.056
High
NO
cohort1
Positve
GAIN
YES
Pre
Female
8
48.13
173.633333
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Left
NO
Mixed
MASTECTOMY
0:Not Recurred
171.35
MB-0207
1
5.05
High
NO
cohort4
Positve
NEUTRAL
YES
Pre
Female
3
43.1
196.866667
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Right
YES
Lobular
BREAST CONSERVING
0:Not Recurred
194.28
MB-7295
7
6.04
Moderate
NO
cohort1
Negative
GAIN
NO
Post
Female
4ER-
77.72
20.2
1:DECEASED
Her2
ER+/HER2- High Prolif
Died of Disease
Left
NO
Ductal/NST
MASTECTOMY
1:Recurred
19.93
MB-0221
0
4.032
High
NO
cohort3
Negative
NEUTRAL
NO
Post
Female
4ER-
63.95
230.5
0:LIVING
claudin-low
ER-/HER2-
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
227.47
MB-4888
3
4.046
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
2
73.55
2.533333
1:DECEASED
LumB
ER+/HER2- Low Prolif
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
2.5
MB-5475
0
3.031
Low
NO
cohort4
Negative
GAIN
YES
Post
Female
5
69.64
105
0:LIVING
Her2
HER2+
Living
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
103.62
MB-7067
1
4.04
Low
YES
cohort1
Positve
NEUTRAL
YES
Pre
Female
3
48.11
138.1
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
136.28
MB-0172
2
5.06
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
7
74.8
41.466667
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
1:Recurred
31.25
MB-4855
0
3.07
High
NO
cohort5
Positve
NEUTRAL
NO
Post
Female
7
82.51
77.5
1:DECEASED
Normal
null
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
76.48
MB-6101
1
4.04
High
NO
cohort5
Positve
NEUTRAL
YES
Post
Female
3
74.43
174.5
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
172.2
MB-6214
0
4.03
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
9
61.16
256.866667
1:DECEASED
LumB
ER+/HER2- High Prolif
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
253.49
MB-4004
0
4.034
High
YES
cohort1
Negative
NEUTRAL
NO
Post
Female
10
52.14
153.966667
0:LIVING
Basal
null
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
151.94
MB-0062
0
4.056
Moderate
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
7
63.2
2
0:LIVING
LumB
ER+/HER2- High Prolif
Living
Right
YES
Mucinous
BREAST CONSERVING
0:Not Recurred
1.97
MB-5525
0
4.068
High
NO
cohort4
Negative
GAIN
YES
Post
Female
5
61.38
186.366667
0:LIVING
Her2
HER2+
Living
Left
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
183.91
MB-7143
0
4.004
Moderate
NO
cohort3
Positve
GAIN
NO
Pre
Female
4ER+
47.35
202.1
0:LIVING
Normal
HER2+
Living
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
199.44
MB-5351
0
2.002
Low
NO
cohort3
Positve
NEUTRAL
NO
Pre
Female
4ER+
40.04
197.433333
0:LIVING
Normal
ER+/HER2- Low Prolif
Living
Left
NO
Mixed
MASTECTOMY
0:Not Recurred
194.84
MB-4897
0
4.026
Moderate
NO
cohort3
Positve
NEUTRAL
NO
Post
Female
3
67.54
185.766667
0:LIVING
LumA
ER+/HER2- High Prolif
Living
Right
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
183.32
MB-5471
2
4.06
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
8
67.12
29.3
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Disease
Left
NO
Ductal/NST
MASTECTOMY
1:Recurred
27.66
MB-4869
0
4.026
High
NO
cohort4
Negative
NEUTRAL
NO
Post
Female
4ER-
65.22
80.233333
0:LIVING
claudin-low
ER-/HER2-
Living
null
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
79.18
MB-7025
1
5.044
High
YES
cohort4
Negative
NEUTRAL
NO
Post
Female
10
57.62
81.033333
0:LIVING
Basal
ER-/HER2-
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
79.97
MB-7038
0
4.042
High
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
1
74.63
104.466667
1:DECEASED
LumB
null
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
103.09
MB-0178
0
4.136
High
NO
cohort3
Positve
NEUTRAL
NO
Post
Female
8
51.29
131.3
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Other Causes
Right
NO
Mixed
MASTECTOMY
0:Not Recurred
129.57
MB-4730
0
4.03
High
YES
cohort1
Negative
NEUTRAL
NO
Post
Female
10
62.81
114.466667
0:LIVING
Basal
ER-/HER2-
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
112.96
MB-0516
0
4.05
High
NO
cohort4
Positve
NEUTRAL
YES
Post
Female
8
63.79
78.166667
0:LIVING
LumB
ER+/HER2- High Prolif
Living
Right
YES
Ductal/NST
BREAST CONSERVING
0:Not Recurred
77.14
MB-7015
2
5.07
High
YES
cohort3
Negative
NEUTRAL
NO
Pre
Female
10
40.21
14.8
1:DECEASED
Basal
ER-/HER2-
Died of Disease
Left
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
10.56
MB-5529
3
2.07
High
NO
cohort3
Positve
NEUTRAL
YES
Post
Female
7
89
45.5
1:DECEASED
LumA
ER+/HER2- High Prolif
Died of Other Causes
Left
NO
Ductal/NST
MASTECTOMY
0:Not Recurred
44.9
MB-4794
0
4.044
High
YES
cohort1
Negative
GAIN
YES
Pre
Female
5
33.83
132.766667
0:LIVING
Her2
HER2+
Living
Left
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
131.02
MB-0479
3
4.05
Moderate
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
8
68.66
186.533333
0:LIVING
LumA
ER+/HER2- Low Prolif
Living
Right
YES
Ductal/NST
MASTECTOMY
0:Not Recurred
184.08
MB-0273
0
4.054
High
NO
cohort3
null
NEUTRAL
NO
Post
Female
9
69.68
210.966667
0:LIVING
Basal
ER-/HER2-
Living
Right
NO
Ductal/NST
MASTECTOMY
1:Recurred
85.26
MB-4880
0
3.142
Moderate
NO
cohort5
Positve
NEUTRAL
YES
Post
Female
2
78.69
60.9
1:DECEASED
LumB
ER+/HER2- Low Prolif
Died of Disease
Left
NO
Mucinous
MASTECTOMY
1:Recurred
60.1
MB-6254
0
2.02
Moderate
NO
cohort1
Positve
NEUTRAL
YES
Post
Female
3
79.38
24.3
1:DECEASED
LumA
ER+/HER2- Low Prolif
Died of Disease
Right
YES
Ductal/NST
BREAST CONSERVING
1:Recurred
23.91
MB-0204
End of preview.

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Check out the documentation for more information.

Flexynesis Benchmark Datasets

Dataset key Biology Modalities Samples Task types available
dataset1 Cancer drug response (CCLE/GDSC cell lines) gex, cnv ~950 / 240 Regression (drug IC50 per compound)
dataset2 Microsatellite instability (MSI) gex, meth ~380 / 95 Binary classification (MSI-H vs MSS)
lgggbm_tcga_pub_processed Brain tumours: LGG + GBM (TCGA) mut, cna 556 / 238 Classification, survival, regression
brca_metabric Breast cancer (METABRIC) gex, cna ~1390 / 595 Classification, survival, regression
singlecell_bonemarrow Bone marrow single-cell RNA gex ~7500 / 2500 Classification, unsupervised

Note on single-cell data: flexynesis was designed for bulk multi-omics data (patient cohorts, cell lines) — not single-cell RNA-seq. It has no built-in handling for the sparsity, scale, or batch structure typical of scRNA-seq. The singlecell_bonemarrow dataset is included as a benchmark curiosity and works well for supervised cell type classification (where cell type labels are available), but flexynesis is not the right tool for unsupervised single-cell analysis, trajectory inference, or integration of large scRNA-seq atlases. For those tasks, use Scanpy/Seurat/scVI instead. If the user's question is specifically about supervised classification of single-cell data with known labels, it is worth trying.

All datasets hosted at https://bimsbstatic.mdc-berlin.de/akalin/buyar/flexynesis-benchmark-datasets/

Reference: Uyar et al., Nature Communications 2025 — https://doi.org/10.1038/s41467-025-63688-5

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