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Datasets:
Synthyra
/
clustered_ppi_string

Tags:
protein-protein-interactions
biology
bioinformatics
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
organism: int64
pos_count: int64
neg_count: int64
pos_prop: double
neg_prop: double
vs
stat: string
value: double
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3608, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2368, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2573, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2082, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 588, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              organism: int64
              pos_count: int64
              neg_count: int64
              pos_prop: double
              neg_prop: double
              vs
              stat: string
              value: double

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Clustered PPI datasets (BIOGRID + STRING) with sequence-disjoint splits

This dataset repo contains multiple dataset variants of protein–protein interactions (PPIs), built by clustering proteins by sequence similarity and then constructing train/valid/test splits that are intended to be disjoint at the protein level (and thus hard to memorize via near-identical sequences).

Artifacts are stored as compressed pickles (*.pkl.gz). A helper downloader exists in this repo:

  • data_processing/download_ppi_data.py::download_clustered_ppi_data

What’s in each split dataframe?

Each split is a pandas.DataFrame with (at minimum):

  • IdA / IdB: protein identifiers
  • OrgA / OrgB: organism identifiers (STRING taxon id for STRING datasets; BIOGRID org id for BIOGRID datasets)
  • labels: >0 indicates a positive interaction, 0 indicates a sampled negative

Some variants also include additional columns (e.g. cluster_a, cluster_b, confidences, org_a, org_b). When negatives are concatenated, some of these columns may be NaN for negative rows.

Dataset variants (index)

A machine-readable index is available at:

  • tables/dataset_index.csv
variant source threshold train rows valid rows test rows train pos rate protein overlap (max)
string_human_st040 string_human st040 12000396 10554 20110 0.500 0

Per-variant deep dive (plots + stats)

Each variant has:

  • plots/<variant>/...png (rendered below)
  • tables/<variant>/summary.csv and tables/<variant>/schema.csv

string_human_st040

Open report

Summary tables

  • tables/string_human_st040/summary.csv
  • tables/string_human_st040/schema.csv

Label balance

  • train: plots/string_human_st040/train_label_counts.png
  • valid: plots/string_human_st040/valid_label_counts.png
  • test: plots/string_human_st040/test_label_counts.png

Organism distributions (positives vs negatives)

  • data: plots/string_human_st040/train_organism_distribution.csv
  • stats: plots/string_human_st040/train_organism_distribution_stats.csv

  • data: plots/string_human_st040/valid_organism_distribution.csv
  • stats: plots/string_human_st040/valid_organism_distribution_stats.csv

  • data: plots/string_human_st040/test_organism_distribution.csv
  • stats: plots/string_human_st040/test_organism_distribution_stats.csv

Cross-split organism shift tests

  • positives: plots/string_human_st040/cross_split_pos_stats.csv
  • negatives: plots/string_human_st040/cross_split_neg_stats.csv

Sequence length distributions (unique proteins)

  • stats: plots/string_human_st040/train_seq_length_stats.csv

  • stats: plots/string_human_st040/valid_seq_length_stats.csv

  • stats: plots/string_human_st040/test_seq_length_stats.csv

Top organism pairs

  • train positives: plots/string_human_st040/train_top_org_pairs_pos.png

  • train negatives: plots/string_human_st040/train_top_org_pairs_neg.png

  • valid positives: plots/string_human_st040/valid_top_org_pairs_pos.png

  • valid negatives: plots/string_human_st040/valid_top_org_pairs_neg.png

  • test positives: plots/string_human_st040/test_top_org_pairs_pos.png

  • test negatives: plots/string_human_st040/test_top_org_pairs_neg.png

How to download and load

Use the helper in this codebase:

from data_processing.download_ppi_data import download_clustered_ppi_data

# BIOGRID example
train_df, valid_df, test_df, interaction_set, seq_dict = download_clustered_ppi_data(
    data_type='biogrid',
    cluster_percentage=0.5,
    hf_repo='Synthyra/clustered_ppi_string',
)

# STRING example (descriptor must match the variant prefix: e.g. 'human' or 'model_orgs')
train_df, valid_df, test_df, interaction_set, seq_dict = download_clustered_ppi_data(
    data_type='string',
    descriptor='human',
    cluster_percentage=0.5,
    hf_repo='Synthyra/clustered_ppi_string',
)
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