9e75f44f1a
Source-only snapshot of the cluster branch for WES execution. Large reference files (HPA/MANE/ensemble FASTA, model weights, ~597 MB) are omitted: they are baked into the container images at build time and mounted from the dreamdock-data PVC at runtime, and exceed the Gitea request size limit. Pipeline entry point is main.nf, which orchestrates the biotransformer, conplex and tissue modules as a single workflow. Ligand inputs are read from the eureka workspace; protein_zarr and chembl_db come from the dreamdock-data PVC.
67 lines
2.9 KiB
Python
67 lines
2.9 KiB
Python
from pathlib import Path
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import argparse
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import pandas as pd
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import numpy as np
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def get_tissue_distribution(file_name):
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# Convert to Path object
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file_path = Path(file_name)
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# Load interaction data — only the 'transcipt' column is referenced downstream
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# (the bulk of the file is the 'smile' column we never use). Skipping it cuts
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# memory ~30x on metabolite-heavy drugs (11 GB file -> ~400 MB resident).
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significant_interactions = pd.read_csv(file_path, sep='\t', usecols=['transcipt'])
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# Check if data exists
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if len(significant_interactions) == 0:
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print(f"No significant interactions found in {file_name}. Creating empty output.")
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tissue_per_prot_symbol_pd = pd.DataFrame()
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save_name = str(file_path.parent / (file_path.stem.replace('_significant_interactions', '_tissue_distribution') + '.tsv'))
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tissue_per_prot_symbol_pd.to_csv(save_name, sep='\t')
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print(f"Saved empty tissue distribution to: {save_name}")
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return
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# Load protein expression per tissue
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HPA = pd.read_csv('/home/omic/HPA_normal_ihc_data.tsv', sep='\t')
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# Load enst symbol mapping data
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MANE_all_transcipts = pd.read_csv('/home/omic/MANE_all_transcipts.csv')
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# Filter out tissues with low confidence
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HPA = HPA[(HPA['Level'] == 'Medium') | (HPA['Level'] == 'High')]
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# Get protein symbols from significant interactions
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prot_enst = [i.split('_')[0] for i in significant_interactions['transcipt'].unique()]
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# Safely map to symbols, skipping missing ones
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prot_symbol = []
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for i in prot_enst:
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matches = MANE_all_transcipts[MANE_all_transcipts['transcipt'] == i]
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if not matches.empty:
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prot_symbol.append(matches['symbol'].iloc[0])
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else:
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print(f"Warning: Transcript {i} not found in MANE mapping. Skipping.")
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prot_symbol = pd.unique(prot_symbol)
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# Get unique tissues
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unique_tissues = HPA['Tissue'].unique()
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# Get protein expression per tissue
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tissue_per_prot_symbol = [list(HPA[HPA['Gene name'] == i]['Tissue'].unique()) for i in prot_symbol]
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# Transform list to array
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tissue_per_prot_symbol_array = np.array([[tissue in gene_tissues for tissue in unique_tissues] for gene_tissues in tissue_per_prot_symbol])
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tissue_per_prot_symbol_pd = pd.DataFrame(tissue_per_prot_symbol_array, columns=unique_tissues, index=prot_symbol)
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# Save
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save_name = str(file_path.parent / (file_path.stem.replace('_significant_interactions', '_tissue_distribution') + '.tsv'))
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tissue_per_prot_symbol_pd.to_csv(save_name, sep='\t')
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print(f"Saved tissue distribution to: {save_name}")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Get tissue distribution from *significant_interactions.tsv.")
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parser.add_argument("--file_name", required=True, help="file_name")
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args = parser.parse_args()
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get_tissue_distribution(args.file_name) |