Note
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Exploring the Challenge Data#
This example shows you how to load and explore the challenge data with biolearn
Loading up the data for the competition#
from biolearn.data_library import DataLibrary
challenge_data = DataLibrary().get("BoAChallengeData").load()
The challenge data has methylation data#
import pandas as pd
pd.options.display.max_columns = 6
challenge_data.dnam
The challenge data also has proteomic data#
You can learn more about what the protein identifies in our reference#
from biolearn.util import get_data_file
reference = pd.read_csv(get_data_file("reference/alamar_reference.csv"))
reference
Some of the data overlaps while some does not but all the metadata is combined#
You can easily run several models on them#
from biolearn.mortality import run_predictions
prediction_dict = {
"Horvathv1": "Predicted",
"Hannum": "Predicted"
}
predictions = run_predictions(challenge_data, prediction_dict)
predictions
We can then compare the output from the two models#
import matplotlib.pyplot as plt
import seaborn as sns
# Assuming your dataframe is named 'df'
# df should have columns 'Horvathv1' and 'Hannum'
# Create a scatter plot with a regression line
plt.figure(figsize=(8, 6))
sns.regplot(x='Horvathv1', y='Hannum', data=predictions, ci=None)
plt.title('Scatter Plot with Regression Line')
plt.xlabel('Horvathv1')
plt.ylabel('Hannum')
plt.grid(True)
plt.show()
Total running time of the script: (0 minutes 20.893 seconds)
Estimated memory usage: 15052 MB
