Datasets:

kangjiezheng
/

MUSS-data

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 9 new columns ({'gene_type', 'transcript_id', 'gene_id', 'gene_name', 'Chromosome', 'Start', 'tss', 'End', 'Strand'}) and 2 missing columns ({'barcode', 'metacell'}).

This happened while the csv dataset builder was generating data using

hf://datasets/kangjiezheng/MUSS-data/reference/gene_intervals.csv (at revision 64656e63e0771b2d65f663379c675e5f4351a3ce), ['hf://datasets/kangjiezheng/MUSS-data@64656e63e0771b2d65f663379c675e5f4351a3ce/raw/pbmc10k/seacell_metacell_assignments.csv', 'hf://datasets/kangjiezheng/MUSS-data@64656e63e0771b2d65f663379c675e5f4351a3ce/reference/gene_intervals.csv', 'hf://datasets/kangjiezheng/MUSS-data@64656e63e0771b2d65f663379c675e5f4351a3ce/reference/hvg_genes.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/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, 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 822, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              transcript_id: string
              Chromosome: string
              Start: int64
              End: int64
              Strand: string
              gene_id: string
              gene_name: string
              gene_type: string
              tss: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1302
              to
              {'barcode': Value('string'), 'metacell': 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 1379, 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 956, 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 1702, 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 1858, 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 9 new columns ({'gene_type', 'transcript_id', 'gene_id', 'gene_name', 'Chromosome', 'Start', 'tss', 'End', 'Strand'}) and 2 missing columns ({'barcode', 'metacell'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/kangjiezheng/MUSS-data/reference/gene_intervals.csv (at revision 64656e63e0771b2d65f663379c675e5f4351a3ce), ['hf://datasets/kangjiezheng/MUSS-data@64656e63e0771b2d65f663379c675e5f4351a3ce/raw/pbmc10k/seacell_metacell_assignments.csv', 'hf://datasets/kangjiezheng/MUSS-data@64656e63e0771b2d65f663379c675e5f4351a3ce/reference/gene_intervals.csv', 'hf://datasets/kangjiezheng/MUSS-data@64656e63e0771b2d65f663379c675e5f4351a3ce/reference/hvg_genes.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.

barcode string	metacell string
AAACAGCCAACAACAA-1___pbmc10k	SEACell-6
AAACAGCCACCGGCTA-1___pbmc10k	SEACell-90
AAACAGCCAGGACACA-1___pbmc10k	SEACell-105
AAACAGCCATCCTAGA-1___pbmc10k	SEACell-67
AAACATGCAAAGGTAC-1___pbmc10k	SEACell-91
AAACATGCAAATTCGT-1___pbmc10k	SEACell-131
AAACATGCAACCGCCA-1___pbmc10k	SEACell-130
AAACATGCACTTGTTC-1___pbmc10k	SEACell-80
AAACATGCAGAAATGC-1___pbmc10k	SEACell-15
AAACATGCAGGCTTGT-1___pbmc10k	SEACell-57
AAACATGCAGGTTACC-1___pbmc10k	SEACell-76
AAACATGCATGAATAG-1___pbmc10k	SEACell-139
AAACATGCATGCATAT-1___pbmc10k	SEACell-35
AAACATGCATTGTTGG-1___pbmc10k	SEACell-99
AAACCAACAACTAACT-1___pbmc10k	SEACell-77
AAACCAACAGGTTTGC-1___pbmc10k	SEACell-32
AAACCAACATGTCAAT-1___pbmc10k	SEACell-40
AAACCAACATGTTTGG-1___pbmc10k	SEACell-6
AAACCGAAGCACTTGG-1___pbmc10k	SEACell-101
AAACCGAAGCCAAATC-1___pbmc10k	SEACell-51
AAACCGAAGCTGGCTA-1___pbmc10k	SEACell-51
AAACCGAAGGACCGCT-1___pbmc10k	SEACell-120
AAACCGAAGTAATCCA-1___pbmc10k	SEACell-118
AAACCGAAGTGAACGA-1___pbmc10k	SEACell-129
AAACCGCGTAAAGCGG-1___pbmc10k	SEACell-25
AAACCGCGTAATCGCA-1___pbmc10k	SEACell-113
AAACCGCGTCAATACG-1___pbmc10k	SEACell-11
AAACCGCGTCCGTAGG-1___pbmc10k	SEACell-60
AAACCGCGTGATTTGG-1___pbmc10k	SEACell-46
AAACCGCGTGCATCGG-1___pbmc10k	SEACell-88
AAACCGCGTGCTAGAC-1___pbmc10k	SEACell-107
AAACCGCGTGGCTTCC-1___pbmc10k	SEACell-31
AAACCGCGTTGCGCGA-1___pbmc10k	SEACell-83
AAACCGCGTTTCGCGC-1___pbmc10k	SEACell-118
AAACCGGCAATTTAGC-1___pbmc10k	SEACell-123
AAACCGGCACTAAGCC-1___pbmc10k	SEACell-100
AAACCGGCAGCTTAAT-1___pbmc10k	SEACell-2
AAACCGGCATTGTTGG-1___pbmc10k	SEACell-76
AAACGCGCATGCTTAG-1___pbmc10k	SEACell-87
AAACGGATCAAAGGCA-1___pbmc10k	SEACell-106
AAACGGATCAAGCCTG-1___pbmc10k	SEACell-133
AAACGGATCCGTCCAT-1___pbmc10k	SEACell-118
AAACGGATCCTCATCA-1___pbmc10k	SEACell-88
AAACGGATCGCTAGAT-1___pbmc10k	SEACell-2
AAACGGATCGTTAACA-1___pbmc10k	SEACell-6
AAACGGATCTAATTGG-1___pbmc10k	SEACell-37
AAACGGATCTGTAATG-1___pbmc10k	SEACell-24
AAACGTACAAATACCT-1___pbmc10k	SEACell-18
AAACGTACAATGAGGT-1___pbmc10k	SEACell-120
AAACGTACACTAGGTC-1___pbmc10k	SEACell-135
AAACGTACAGCACGAA-1___pbmc10k	SEACell-120
AAACGTACATTGTGGC-1___pbmc10k	SEACell-111
AAAGCAAGTACCGTAA-1___pbmc10k	SEACell-104
AAAGCAAGTACGGGTT-1___pbmc10k	SEACell-96
AAAGCAAGTGAGGTAG-1___pbmc10k	SEACell-71
AAAGCAAGTTCATCTA-1___pbmc10k	SEACell-31
AAAGCACCAAGATTCT-1___pbmc10k	SEACell-99
AAAGCACCAAGTGAAC-1___pbmc10k	SEACell-71
AAAGCACCAGCTTAAT-1___pbmc10k	SEACell-39
AAAGCACCATAGACTT-1___pbmc10k	SEACell-92
AAAGCCCGTACCTTAC-1___pbmc10k	SEACell-2
AAAGCCCGTCCACAAA-1___pbmc10k	SEACell-104
AAAGCCCGTCCGTAGG-1___pbmc10k	SEACell-20
AAAGCCCGTCCTTTAA-1___pbmc10k	SEACell-138
AAAGCCCGTCTAACAG-1___pbmc10k	SEACell-77
AAAGCCCGTGAGAAAC-1___pbmc10k	SEACell-18
AAAGCCCGTGAGGTAG-1___pbmc10k	SEACell-18
AAAGCCCGTTCCTCCT-1___pbmc10k	SEACell-18
AAAGCCCGTTTCAGGA-1___pbmc10k	SEACell-23
AAAGCCGCAAGGCCAA-1___pbmc10k	SEACell-31
AAAGCCGCAATATAGG-1___pbmc10k	SEACell-75
AAAGCCGCAGGCATGA-1___pbmc10k	SEACell-117
AAAGCCGCAGGCCATT-1___pbmc10k	SEACell-115
AAAGCCGCATAGCGAG-1___pbmc10k	SEACell-18
AAAGCGGGTGCGCGTA-1___pbmc10k	SEACell-28
AAAGCGGGTGCTCACC-1___pbmc10k	SEACell-11
AAAGCGGGTTAACAGT-1___pbmc10k	SEACell-74
AAAGCGGGTTAGTACG-1___pbmc10k	SEACell-24
AAAGCTTGTACCCACC-1___pbmc10k	SEACell-86
AAAGCTTGTGATCATG-1___pbmc10k	SEACell-95
AAAGCTTGTGGATTGC-1___pbmc10k	SEACell-75
AAAGCTTGTTAAGGTT-1___pbmc10k	SEACell-13
AAAGCTTGTTTAGCTC-1___pbmc10k	SEACell-139
AAAGCTTGTTTCGCGC-1___pbmc10k	SEACell-66
AAAGGACGTTGGGTTA-1___pbmc10k	SEACell-64
AAAGGACGTTTAAAGC-1___pbmc10k	SEACell-128
AAAGGAGCAATATGGA-1___pbmc10k	SEACell-57
AAAGGAGCACTTCACT-1___pbmc10k	SEACell-118
AAAGGAGCATAAACCT-1___pbmc10k	SEACell-52
AAAGGAGCATAATTGC-1___pbmc10k	SEACell-58
AAAGGAGCATGAGCAG-1___pbmc10k	SEACell-84
AAAGGCTCAAGGTGCA-1___pbmc10k	SEACell-135
AAAGGCTCAATAGCAA-1___pbmc10k	SEACell-81
AAAGGCTCACCAACCG-1___pbmc10k	SEACell-6
AAAGGCTCACCCTCAC-1___pbmc10k	SEACell-100
AAAGGCTCATGCATAT-1___pbmc10k	SEACell-50
AAAGGTTAGAACAAGT-1___pbmc10k	SEACell-116
AAAGGTTAGCTCAAAC-1___pbmc10k	SEACell-107
AAAGGTTAGGACAATG-1___pbmc10k	SEACell-3
AAAGGTTAGGACCTCA-1___pbmc10k	SEACell-133

End of preview.

MUSS data

Data and reference files for MUSS: A Multi-scale and Sequence-based Model for Single-cell Gene Regulation (NeurIPS 2026).

Code and reproduction guide · Model checkpoints

Directory	Contents and original source
`raw/pbmc10k/`	Paired ATAC/GEX matrices and SEACells assignments; 10x Genomics
`raw/pbmc169k/`	Matrices for 47 batches; De Rop et al., GSE194028
`raw/hca_bone/`	Paired matrices for 48 samples and consensus peaks; To et al., E-MTAB-14385, atlas downloads
`reference/`	GRCh38 genome, gene intervals, selected 5,257-gene MUSS HVG list and sequence folds

The raw/ files are preprocessing input matrices and annotations, rather than sequencing reads. For HCA bone, use the supplied MUSS inputs; the original study's processed download differs. Download BMMC and Open Problems inputs separately from GEO GSE194122 and Kaggle, following the code guide.

Download and preprocess

Run these commands in the environment from the code repository:

export MUSS_ASSETS="$PWD/../MUSS_assets"
hf download kangjiezheng/MUSS-data --repo-type dataset --local-dir "$MUSS_ASSETS"
hf download kangjiezheng/MUSS --include "checkpoints/*" --local-dir "$MUSS_ASSETS"

To omit HCA bone, add --exclude "raw/hca_bone/*" to the data download. Every dataset requires local preprocessing, even when using released checkpoints. Follow the preprocessing commands to build PBMC first, then the other datasets using its gene panel and embeddings.

Datasets retain their source terms. reference/sequences_human.bed is from Borzoi (Apache 2.0).

Downloads last month: 20

Total file size:

185 GB

Models trained or fine-tuned on kangjiezheng/MUSS-data

kangjiezheng/MUSS

Updated 1 day ago • 1

MUSS data

Contents

Download and preprocess

Models trained or fine-tuned on kangjiezheng/MUSS-data