143 lines
3.9 KiB
Python
143 lines
3.9 KiB
Python
import marimo
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__generated_with = "0.14.16"
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app = marimo.App(width="medium")
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@app.cell
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def _():
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import json
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import marimo as mo
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import pandas as pd
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import numpy as np
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return json, mo, np, pd
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@app.cell
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def _(mo):
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mo.md(
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r"""
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# Validate the Json created by the pgx-pipeline
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This notebook is to validate the json created by the scripts in the pgx-pipeline repo with the data from [pharmvar](https://www.pharmvar.org/).
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"""
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)
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return
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@app.cell
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def _(pd):
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# original file downloaded from pharmvar for NAT2
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pharmvar = pd.read_csv(
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"../data/NAT2-6.2.15/GRCh38/NAT2.NC_000008.11.haplotypes.tsv",
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sep="\t",
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comment="#",
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)
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pharmvar.head(10)
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return (pharmvar,)
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@app.cell
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def _(json):
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# json created with the scripts in the PGx-engine using above file
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with open("../data/NAT2_translation.json", "r") as handle:
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json_data = json.load(handle)
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print(json_data.keys())
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# parse vars into list to use later for mapping
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j_var_list = [
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[str(j["position"]), j["rsid"], j["ref"], j["alts"]]
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for j in json_data["variants"]
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]
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print(j_var_list[0])
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print(len(j_var_list))
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return j_var_list, json_data
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@app.cell
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def _(j_var_list, json_data, np):
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# extract the allele info for each allele and map with the variant details
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allele_dict = {}
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for allele in json_data["namedAlleles"]:
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allele_id = allele["id"]
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# if allele has no variants (i.e. wild type) give it a fixed entry
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allele_dict[allele_id] = (
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[
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j_var_list[idx]
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for idx, var in enumerate(allele["alleles"])
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if var != None
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]
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if allele_id not in ("NAT2*1", "NAT2*1.001")
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else [[".", np.nan, np.nan, [np.nan]]]
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)
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allele_dict["NAT2*4.002"]
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return (allele_dict,)
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@app.cell
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def _(allele_dict, pharmvar):
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# check allele names match between json and the original file
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pharmvar_hap_names = pharmvar["Haplotype Name"].unique().tolist()
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json_hap_names = list(allele_dict.keys())
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# check allele ids are same between json and pharvar sources
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assert len(pharmvar_hap_names) == len(json_hap_names)
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assert set(pharmvar_hap_names) == set(json_hap_names)
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return (pharmvar_hap_names,)
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@app.cell
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def _(pharmvar, pharmvar_hap_names):
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# parse the pharmvar table entries for each allele into a list for comparison with the json data
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def get_pharmvar_vars(pharmvar_df, star_id):
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vals = pharmvar_df.loc[pharmvar_df["Haplotype Name"] == star_id][
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["Variant Start", "rsID", "Reference Allele", "Variant Allele"]
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].values
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return [[v[0], v[1], v[2], [v[3]]] for v in vals]
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pharmvar_dict = {}
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for name in pharmvar_hap_names:
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pharmvar_dict[name] = get_pharmvar_vars(pharmvar, name)
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pharmvar_dict["NAT2*4.002"]
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return (pharmvar_dict,)
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@app.cell
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def _(allele_dict, pd, pharmvar_dict):
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# compare the json to what is in the pharmvar
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results = []
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for pharm_name, pharm_data in pharmvar_dict.items():
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if sorted(pharm_data) == sorted(allele_dict[pharm_name]):
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# print(f"{pharm_name}\t\tMATCH")
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results.append([pharm_name, "MATCH", ".", "."])
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else:
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# print([pharm_name, "NO MATCH", pharm_data, allele_dict[pharm_name]])
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results.append(
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[pharm_name, "NO MATCH", pharm_data, allele_dict[pharm_name]]
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)
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df = pd.DataFrame(
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results, columns=["Haplotype Name", "Match", "PhamVar Data", "Json Data"]
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)
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df.value_counts("Match")
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return
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@app.cell
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def _(mo):
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mo.md(
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r"""
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## Conclusions
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- Alleles are all in the json from the Phamvar table.
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- Allele definitions were correctly copied over.
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- Scripts appear to make a correct form of the pharmvar data into json.
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"""
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)
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return
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if __name__ == "__main__":
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app.run()
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