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Datasets:
yeruihku
/
bulkgpt-brca

Tasks:
Other
Languages:
English
Tags:
deconvolution
bulk-rna-seq
single-cell
breast-cancer
tcga
metabric
License:
Dataset card Data Studio Files
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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 1 new columns ({'Unnamed: 0'}) and 1 missing columns ({'sample'}).

This happened while the csv dataset builder was generating data using

hf://datasets/yeruihku/bulkgpt-brca/metabric_props/dtangle__gse176078.csv (at revision 6cdcd2d29d458709bc763617cb0856f6b3a6d34c), ['hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/metabric_props/cibersortx__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/metabric_props/dtangle__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/metabric_props/music__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/cibersortx__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/cibersortx__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/decode__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/decode__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/deseq2__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/deseq2__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/dtangle__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/dtangle__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__frozen__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__frozen__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__ft__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__ft__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/music__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/music__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scaden__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scaden__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__frozen__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__frozen__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__ft__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__ft__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/stack__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/stack__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/tf__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/tf__gse176078.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 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: string
              B-cells: double
              CAFs: double
              Cancer Epithelial: double
              Endothelial: double
              Myeloid: double
              Normal Epithelial: double
              PVL: double
              Plasmablasts: double
              T-cells: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1479
              to
              {'sample': Value('string'), 'B-cells': Value('float64'), 'CAFs': Value('float64'), 'Cancer Epithelial': Value('float64'), 'Endothelial': Value('float64'), 'Myeloid': Value('float64'), 'Normal Epithelial': Value('float64'), 'PVL': Value('float64'), 'Plasmablasts': Value('float64'), 'T-cells': 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 1 new columns ({'Unnamed: 0'}) and 1 missing columns ({'sample'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/yeruihku/bulkgpt-brca/metabric_props/dtangle__gse176078.csv (at revision 6cdcd2d29d458709bc763617cb0856f6b3a6d34c), ['hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/metabric_props/cibersortx__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/metabric_props/dtangle__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/metabric_props/music__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/cibersortx__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/cibersortx__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/decode__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/decode__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/deseq2__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/deseq2__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/dtangle__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/dtangle__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__frozen__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__frozen__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__ft__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/geneformer__ft__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/music__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/music__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scaden__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scaden__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__frozen__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__frozen__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__ft__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/scgpt__ft__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/stack__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/stack__gse176078.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/tf__gse161529.csv', 'hf://datasets/yeruihku/bulkgpt-brca@6cdcd2d29d458709bc763617cb0856f6b3a6d34c/tcga_props/tf__gse176078.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.

sample
string
B-cells
float64
CAFs
float64
Cancer Epithelial
float64
Endothelial
float64
Myeloid
float64
Normal Epithelial
float64
PVL
float64
Plasmablasts
float64
T-cells
float64
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MB-0066
0
0.286835
0.279495
0.034175
0.203874
0
0
0.195619
0
MB-0144
0
0.286544
0.348738
0
0.195162
0
0.150362
0.019195
0
MB-0596
0
0.464772
0.091825
0
0.124536
0
0
0.318867
0
MB-0164
0
0.375449
0.126165
0
0.334012
0
0.071846
0.040124
0.052403
MB-0215
0
0.380149
0.25655
0
0.091487
0
0.091297
0.180517
0
MB-0146
0
0.10979
0.590219
0
0.155934
0
0.144058
0
0
MB-0229
0
0.486628
0.281787
0
0.111124
0
0.01226
0.108202
0
MB-0505
0
0.490327
0.126487
0
0.114247
0
0.187806
0.081132
0
MB-0102
0
0.235675
0.227087
0
0.028461
0
0.136646
0.37213
0
MB-0569
0
0.418197
0.118176
0
0.188134
0
0
0.275494
0
MB-0516
0
0.292377
0.385216
0
0.259237
0
0.053461
0
0.009709
MB-0272
0
0.198727
0.105768
0
0.189502
0
0.127142
0.378861
0
MB-0585
0
0.473413
0.288398
0
0.158553
0
0.079636
0
0
MB-0494
0
0.327536
0.087966
0
0.327679
0
0.031654
0.225165
0
MB-0306
0
0.377792
0.319035
0
0.099589
0
0
0.203584
0
MB-0463
0.008803
0.49915
0.286177
0
0.048686
0
0.106492
0.050692
0
MB-0198
0
0.502255
0.311657
0
0.081645
0
0.104443
0
0
MB-0203
0
0.342232
0.289099
0
0.201418
0
0.127526
0.039724
0
MB-0607
0
0.357887
0.349801
0
0.126949
0
0.099933
0.06543
0
MB-0631
0
0.458072
0.10895
0
0.159452
0
0.160315
0.113212
0
MB-0363
0
0.168282
0.193904
0.040831
0.08595
0
0.136015
0.356118
0.0189
MB-0427
0
0.074174
0.312738
0
0.140078
0
0.158251
0.205682
0.109078
MB-0519
0
0.257325
0.218611
0.049757
0.078754
0
0.079208
0.257295
0.05905
MB-0371
0
0.596562
0.055136
0
0.277543
0
0.041887
0.028872
0
MB-0380
0
0.100254
0.383514
0
0.176903
0
0.113928
0.225401
0
MB-0221
0
0.219417
0.368505
0
0.059831
0
0
0.352247
0
MB-0348
0
0.226795
0.511914
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0.109081
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0.122271
0.029727
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MB-0261
0
0.355705
0.271157
0
0.226693
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0.116472
0.029973
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MB-0576
0
0.411996
0.263972
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0.173488
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0.142079
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MB-0385
0.026202
0.16118
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MB-0659
0.234846
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MB-0379
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0.471023
0.127427
0.007161
0.178333
0
0.118444
0.097612
0
MB-0432
0
0.394183
0
0
0.605817
0
0
0
0
MB-0527
0
0.421593
0
0
0.140279
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0.168655
0.269473
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0.489253
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0.092175
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0
End of preview.

BulkGPT BRCA Downstream Data

Processed expression + clinical data and per-method deconvolution results for the breast-cancer (BRCA) downstream analysis in the BulkGPT deconvolution benchmark. Companion code: RainyEricYe/bulkgpt-deconvolution-benchmark.

Contents

input/ — bulk expression + clinical (h5ad) and scRNA-seq references

File Size Description
tcga_brca_bulk.h5ad 243 MB TCGA-BRCA bulk expression + clinical. 1,095 tumor samples × 60,660 genes. .obs holds 16 clinical columns (ER/PR/HER2 status, survival, recurrence, stage, etc.).
metabric_brca_bulk.h5ad 324 MB METABRIC bulk expression + clinical. 1,980 samples × 20,385 genes. .obs holds 12 clinical columns (ER/PR/HER2, grade, tumor stage).
scRNAseq_ref_gse176078.h5ad 844 MB Default scRNA-seq reference: GSE176078 (Wu et al. 2021) breast cancer. 100,064 cells × 28,468 genes, annotated with celltype_major.
scRNAseq_ref_gse161529.h5ad 8.9 GB Independent scRNA-seq reference: GSE161529 (Pal et al. 2021) breast cancer. 388,167 cells × 29,448 genes. Used for cross-validation.

tcga_props/ — per-method deconvolution proportions for TCGA-BRCA

Each file is the estimated cell-type proportions for the TCGA-BRCA bulk samples, named {method}[__ft|__frozen]__{ref}.csv:

  • method: cibersortx, decode, deseq2, dtangle, geneformer, music, scaden, scgpt, stack, tf
  • ft / frozen: fine-tuned vs frozen-embedding variants (only geneformer and scgpt have both; others are the default variant)
  • ref: gse176078 (default reference) or gse161529 (independent cross-validation reference)

Rows = TCGA-BRCA samples (TCGA-*), columns = 9 cell types (B-cells, CAFs, Cancer Epithelial, Endothelial, Myeloid, Normal Epithelial, PVL, Plasmablasts, T-cells). Values sum to ~1 per sample.

metabric_props/ — per-method deconvolution proportions for METABRIC

Same naming convention as tcga_props/. METABRIC was deconvolved with the default reference (GSE176078) by three methods:

  • cibersortx__gse176078.csv — CIBERSORTx
  • music__gse176078.csv — MuSiC
  • dtangle__gse176078.csv — dtangle

Rows = METABRIC samples (MB-*), columns = same 9 cell types.

Naming convention 

input/  : {cohort}_brca_bulk.h5ad | scRNAseq_ref_gse{accession}.h5ad
props/  : {method}[__ft|__frozen]__gse{accession}.csv
  • gse176078 = Wu et al. 2021 (default reference)
  • gse161529 = Pal et al. 2021 (independent cross-validation reference)

Cell types

The 9 cell types across all proportion files: B-cells, CAFs, Cancer Epithelial, Endothelial, Myeloid, Normal Epithelial, PVL, Plasmablasts, T-cells.

Usage 

pip install huggingface_hub anndata pandas

# Download everything
huggingface-cli download yeruihku/bulkgpt-brca --repo-type dataset --local-dir data/brca

# Load a proportion table
import pandas as pd
df = pd.read_csv("data/brca/tcga_props/scgpt__frozen__gse176078.csv", index_col=0)

# Load a bulk h5ad (expression + clinical)
import anndata as ad
adata = ad.read_h5ad("data/brca/input/tcga_brca_bulk.h5ad")

Citation 

@software{bulkgpt2026,
  author = {Ye, Rui},
  title = {Multi-Foundation Model Benchmark for Bulk RNA-seq Deconvolution},
  year = {2026},
  url = {https://github.com/RainyEricYe/bulkgpt-deconvolution-benchmark}
}

License

CC BY 4.0.
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