“Transcriptomic Clock” using GEO Data

This example loads transcriptomic data from GEO, calculates the predicted age using a transcriptomic clock, and plots it against chronological age.

First load up some transcriptomic data from GEO using the data library

from biolearn.data_library import DataLibrary

data = DataLibrary().get("GSE134080").load()

Now run the transcriptomic clock to predict age

from biolearn.model_gallery import ModelGallery

rna_model = ModelGallery().get("TranscriptomicPredictionModel", imputation_method="none")
predicted =  rna_model.predict(data)

Plot the results against the chronological age using seaborn

import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr

predicted_values = predicted['Predicted']
true_values = data.metadata['age']

correlation, _ = pearsonr(true_values, predicted_values)
print(f"Pearson Correlation Coefficient: {correlation}")

sns.set_theme(style="whitegrid")
sns.regplot(x=true_values, y=predicted_values, scatter_kws={'alpha':0.5})
plt.title('Transcriptomic Prediction Model')
plt.xlabel('Chronological Age')
plt.ylabel('Transcriptomic Age')
plt.show()

Pearson Correlation Coefficient: 0.7025211774998293

Estimated memory usage: 490 MB