SDK Examples

import bioquery
 
client = bioquery.Client()
 
# Simple question
card = client.query("What is the TP53 mutation rate in lung cancer?")
print(card.answer)

card = client.query("Is DDR1 expression higher in KIRP vs KIRC?")
 
print(f"Answer: {card.answer}")
print(f"P-value: {card.p_value}")
print(f"Effect size: {card.effect_size}")
 
# Show the box plot
card.show_figure()

card = client.query("Is TP53 higher in lung cancer vs normal lung?")
 
# Get the data for custom analysis
df = card.to_dataframe()
print(df.groupby("source")["expression"].describe())

card = client.query("Does high EGFR expression affect survival in LUAD?")
 
print(f"Hazard ratio: {card.statistics['hazard_ratio']}")
print(f"95% CI: [{card.statistics['ci_lower']}, {card.statistics['ci_upper']}]")
 
# Save Kaplan-Meier curve
card.save_figure("survival_curve.svg")

card = client.query("Show DDR1 expression across all TCGA cancers")
 
# Get ranked data
df = card.to_dataframe()
print(df.groupby("cancer_type")["expression"].median().sort_values(ascending=False))

import pandas as pd
 
genes = ["DDR1", "EGFR", "TP53", "BRCA1", "KRAS"]
results = []
 
for gene in genes:
    card = client.query(f"Is {gene} higher in KIRP vs KIRC?")
    results.append({
        "gene": gene,
        "p_value": card.p_value,
        "effect_size": card.effect_size,
        "significant": card.p_value < 0.05
    })
 
df = pd.DataFrame(results)
print(df.sort_values("p_value"))

card = client.query("Compare BRCA1 in breast cancer subtypes")
 
# High-resolution figure
card.save_figure(
    "figure1.png",
    width=2400,
    height=1600,
    scale=3.0
)
 
# Vector format
card.save_figure("figure1.svg")
 
# Get methods text
print("Methods section:")
print(card.methods_text)

card = client.query("Show BRAF expression across melanoma cell lines")
card.show_figure()
 
# Get specific cell lines
df = card.to_dataframe()
high_expr = df[df["expression"] > df["expression"].median()]
print(high_expr["cell_line"].tolist())

card = client.query("Compare EGFR protein levels in lung cancer vs normal")
 
print(f"Protein fold change: {card.statistics.get('fold_change', 'N/A')}")

import asyncio
from bioquery import AsyncClient
 
async def analyze_genes(genes):
    async with AsyncClient() as client:
        tasks = [client.query(f"Is {g} higher in KIRP vs KIRC?") for g in genes]
        cards = await asyncio.gather(*tasks)
        return cards
 
genes = ["DDR1", "EGFR", "TP53"]
cards = asyncio.run(analyze_genes(genes))
 
for card in cards:
    print(f"{card.question}: p={card.p_value}")

from bioquery.exceptions import QueryError, RateLimitError
import time
 
def safe_query(client, question, retries=3):
    for attempt in range(retries):
        try:
            return client.query(question)
        except RateLimitError:
            if attempt < retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise
        except QueryError as e:
            print(f"Query failed: {e}")
            return None
 
card = safe_query(client, "Is DDR1 higher in KIRP vs KIRC?")

import json
 
# Save card to file
card = client.query("Is DDR1 higher in KIRP vs KIRC?")
with open("my_analysis.json", "w") as f:
    f.write(card.to_json())
 
# Load and recreate (for reference)
with open("my_analysis.json") as f:
    data = json.load(f)
    print(f"Original question: {data['question']}")
    print(f"Answer: {data['answer']}")