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75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
from io import StringIO
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import json
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import pandas
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import requests
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# Genes of interest (in this case, genes involved in glycolysis according to
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# the KEGG pathways database [KEGG:hsa00010+M00001]).
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genes = [
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"ENSG00000111640",
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"ENSG00000111669",
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"ENSG00000149925",
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"ENSG00000074800",
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"ENSG00000105220",
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"ENSG00000067225",
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"ENSG00000102144",
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"ENSG00000141959",
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"ENSG00000156515",
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"ENSG00000171314",
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"ENSG00000067057",
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"ENSG00000111674",
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"ENSG00000159322",
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"ENSG00000152556",
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"ENSG00000109107",
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"ENSG00000159399",
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"ENSG00000108515",
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"ENSG00000160883",
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"ENSG00000226784",
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"ENSG00000188316",
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"ENSG00000106633",
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"ENSG00000136872",
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"ENSG00000156510",
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"ENSG00000143627",
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"ENSG00000170950",
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]
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# Get a summary on the ubiquity of the above gene set:
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response_summary = requests.post(
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"https://ubigen.uni-rostock.de/api/summary",
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headers={"Content-Type": "text/plain"},
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data=" ".join(genes),
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)
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summary = json.loads(response_summary.content)
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median_percentile = summary["median_percentile"]
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median_percentile_rounded = round(median_percentile, 3)
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estimated_change = summary["change"]
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p_value = summary["p_value"]
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# Example information: Median percentile of the genes and p-value for difference
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# in scores in comparison with other genes.
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print(f"Median percentile: {median_percentile_rounded * 100}%")
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print(f"Estimated score difference: {estimated_change} (p = {p_value})")
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# Retrieve detailed information on all parameters and the ranking of the gene
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# set defined above:
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response_ranking = requests.post(
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"https://ubigen.uni-rostock.de/api/ranking",
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headers={"Content-Type": "text/plain"},
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data=" ".join(genes),
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)
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# Parse the data using pandas. This gives lots of opportunity for further
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# analyses.
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data = pandas.read_csv(StringIO(response_ranking.text))
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# Example analysis: Recompute the mean percentile using pandas.
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median_percentile_new = data["percentile"].median()
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median_percentile_new_rounded = round(median_percentile_new, 3)
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print(f"Recomputed median percentile: {median_percentile_new_rounded * 100}%")
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