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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 2 new columns ({'CD3', 'Patch'}) and 1 missing columns ({'CD8'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mittal-research-lab/PhenoBIC-Data/ground_truth/external_val_TMA_cores/TMA1_CD3.csv (at revision 6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698), ['hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_CD3.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_CD45RO.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_CD8.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_Foxp3.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_CD163CD68.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_CD20.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_CD8.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_PD1.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_CD8.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_Ki67.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_PDL1.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_PanCK.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/internal_val_WSIs/WSI1.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/internal_val_WSIs/WSI2.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/internal_val_WSIs/WSI3.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 1837, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
Patch: string
CD3: string
label: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 604
to
{'CD8': Value('string'), 'label': Value('int64')}
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 1683, 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 1839, 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 2 new columns ({'CD3', 'Patch'}) and 1 missing columns ({'CD8'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mittal-research-lab/PhenoBIC-Data/ground_truth/external_val_TMA_cores/TMA1_CD3.csv (at revision 6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698), ['hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_CD3.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_CD45RO.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_CD8.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA1_Foxp3.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_CD163CD68.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_CD20.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_CD8.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA2_PD1.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_CD8.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_Ki67.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_PDL1.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/external_val_TMA_cores/TMA3_PanCK.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/internal_val_WSIs/WSI1.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/internal_val_WSIs/WSI2.csv', 'hf://datasets/mittal-research-lab/PhenoBIC-Data@6bf7cc0b269b86a62cb19a7d0b4ca4ec590e5698/ground_truth/internal_val_WSIs/WSI3.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.
CD8 string | label int64 |
|---|---|
neg | 8,249 |
neg | 8,250 |
neg | 8,251 |
neg | 8,252 |
pos | 8,253 |
neg | 8,254 |
neg | 8,255 |
neg | 8,256 |
neg | 8,257 |
neg | 8,258 |
neg | 8,259 |
neg | 8,260 |
neg | 8,261 |
neg | 8,262 |
neg | 8,263 |
neg | 8,264 |
neg | 8,265 |
neg | 8,266 |
neg | 8,267 |
neg | 8,268 |
neg | 8,269 |
neg | 8,270 |
neg | 8,271 |
neg | 8,272 |
neg | 8,273 |
neg | 8,274 |
neg | 8,275 |
neg | 8,276 |
neg | 8,277 |
neg | 8,278 |
neg | 8,279 |
neg | 8,280 |
neg | 8,281 |
neg | 8,282 |
neg | 8,283 |
neg | 8,284 |
neg | 8,285 |
neg | 8,286 |
neg | 8,638 |
neg | 8,639 |
neg | 8,643 |
neg | 8,649 |
neg | 8,652 |
neg | 8,654 |
pos | 8,657 |
neg | 8,658 |
neg | 8,659 |
neg | 8,661 |
neg | 8,664 |
neg | 8,670 |
neg | 8,671 |
neg | 8,672 |
neg | 8,675 |
neg | 8,677 |
neg | 8,678 |
neg | 8,681 |
neg | 8,683 |
neg | 8,685 |
neg | 8,686 |
neg | 8,689 |
neg | 8,690 |
neg | 8,692 |
neg | 8,694 |
neg | 8,695 |
neg | 8,696 |
neg | 8,701 |
neg | 8,703 |
neg | 8,710 |
neg | 8,711 |
neg | 8,712 |
neg | 8,715 |
neg | 8,717 |
neg | 8,719 |
neg | 8,720 |
neg | 8,721 |
neg | 8,722 |
neg | 8,726 |
neg | 8,734 |
neg | 8,738 |
neg | 8,740 |
neg | 8,744 |
neg | 8,749 |
neg | 8,751 |
neg | 8,758 |
neg | 8,759 |
neg | 8,761 |
neg | 8,770 |
neg | 8,772 |
neg | 8,773 |
neg | 8,786 |
neg | 8,791 |
neg | 8,819 |
neg | 8,827 |
neg | 8,829 |
neg | 8,832 |
neg | 8,843 |
neg | 8,849 |
neg | 8,852 |
neg | 8,855 |
neg | 8,856 |
End of preview.
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Check out the documentation for more information.
About
These datasets were acquired and annotated for the purpose of training and evaluating the PhenoBIC model. PhenoBIC is a deep learning model that predicts marker co-expression phenotype of cells by performing image classification of the biomarker "imprint" (staining pattern) of the cell. This enables automated and operator-independent cell biomarker expression classification with spatial multiplex imaging data. These data are multiplex images of breast tissue with cell segmentations that have been manually annotated (with independent quality controlled reviews) for the cell expression status of multiple biomarkers.
training_WSIs.zip contains CD8 channel fields of view (FOV) across three whole slide images (WSIs) that were stained with the Opal™ 9-color workflow (Quanterix) and imaged using PhenoImager™ HT (Quanterix), previously referred to as Vectra Polaris (Akoya Biosciences). These were used for PhenoBIC model training.
training_WSIs/
├── cell_segmentations
│ ├── "WSI1.tiff"
│ ├── "WSI2.tiff"
| ├── "WSI3.tiff"
├── multiplex_images
│ ├── "WSI1_CD8.tiff"
│ ├── "WSI2_CD8.tiff"
| ├── "WSI3_CD8.tiff"
internal_val_WSIs.zip contains CD8 channel FOVs across three other patient WSIs. These were used for PhenoBIC model validation.
internal_val_WSIs/
├── "WSI#1"
│ ├── cell_segmentations
│ │ ├── "FOV#1.tiff"
│ │ ├── "FOV#2.tiff"
| | ├── ...
│ ├── multiplex_images
│ │ ├── "FOV#1"
│ │ │ ├── CD8.tif
│ │ ├── "FOV#2"
│ │ │ ├── CD8.tif
| | ├── ...
├── "WSI#2" ...
├── "WSI#3" ...
external_val_TMA_cores.zip contains multiple channels of sampled cores across three multiplex breast tissue microarrays (TMAs). TMA#1 and TMA#2 were acquired with Vectra while TMA#3 was acquired with PhenoCycler® (Quanterix), formerly known as CODEX (Akoya Biosciences), imaging platform. These were used for PhenoBIC model validation.
external_val_TMA_cores/
├── "TMA#1_channel#1"
│ ├── "core#1"
│ │ ├── cell_segmentations
│ │ │ ├── "core#1.tiff"
│ │ ├── multiplex_images
│ │ │ ├── "core#1"
│ │ │ │ ├── "channel#1.tif"
├── "TMA#1_channel#2" ...
├── "TMA#1_channel#3" ...
├── "TMA#1_channel#4" ...
├── "TMA#2_channel#1" ...
├── "TMA#2_channel#2" ...
├── "TMA#2_channel#3" ...
├── "TMA#2_channel#4" ...
├── "TMA#3_channel#1" ...
├── "TMA#3_channel#2" ...
├── "TMA#3_channel#3" ...
├── "TMA#3_channel#4" ...
ground_truth contains the manually curated labels of cell marker expression for the datasets. Each CSV file contains a column called "label" corresponding a unique Cell ID and another column denoting the annotated marker expression label as "pos" (marker +ve) or "neg" (marker -ve).
ground_truth/
├── external_val_TMA_cores
│ ├── "TMA#1_channel#1.csv"
│ ├── "TMA#1_channel#2.csv"
│ ├── "TMA#1_channel#3.csv"
│ ├── "TMA#1_channel#4.csv"
│ ├── "TMA#2_channel#1.csv"
│ ├── "TMA#2_channel#2.csv"
│ ├── "TMA#2_channel#3.csv"
│ ├── "TMA#2_channel#4.csv"
│ ├── "TMA#3_channel#1.csv"
│ ├── "TMA#3_channel#2.csv"
│ ├── "TMA#3_channel#3.csv"
│ ├── "TMA#3_channel#4.csv"
├── internal_val_WSIs
│ ├── "WSI#1.csv"
│ ├── "WSI#2.csv"
│ ├── "WSI#3.csv"
├── training_WSIs
│ ├── "WSI#1.csv"
│ ├── "WSI#2.csv"
│ ├── "WSI#3.csv"
Citing
Please cite the following if you use this data:
- PhenoBIC paper: Sankaranarayanan, A. et al. PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns. bioRxiv 2026. https://www.biorxiv.org/content/10.64898/2026.06.11.731702v1
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