Version Compatibility

Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scipy 1.12+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Longitudinal Monitoring

"Track ctDNA levels over my patient's treatment" → Monitor tumor fraction and mutation dynamics across serial liquid biopsy timepoints for treatment response assessment and early relapse detection.

• Python: pandas + matplotlib for trend analysis and molecular response classification

Track ctDNA dynamics over treatment for response assessment and relapse detection.

Key Metrics

Metric	Description	Clinical Relevance
Tumor fraction trend	Change over time	Response/progression
Mutation clearance	Time to undetectable	Depth of response
Molecular relapse	ctDNA rise	Early relapse detection
Lead time	ctDNA vs imaging	Months before clinical

Tracking Tumor Fraction

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


def analyze_tf_dynamics(patient_data):
    '''
    Analyze tumor fraction dynamics over treatment.

    Args:
        patient_data: DataFrame with columns [sample_id, timepoint, tumor_fraction, treatment_phase]
    '''
    # Sort by timepoint
    patient_data = patient_data.sort_values('timepoint')

    # Calculate log2 fold changes
    baseline_tf = patient_data.iloc[0]['tumor_fraction']
    patient_data['log2_fc'] = np.log2(patient_data['tumor_fraction'] / baseline_tf)

    # Calculate response metrics
    min_tf = patient_data['tumor_fraction'].min()
    min_timepoint = patient_data.loc[patient_data['tumor_fraction'].idxmin(), 'timepoint']

    metrics = {
        'baseline_tf': baseline_tf,
        'nadir_tf': min_tf,
        'nadir_timepoint': min_timepoint,
        'max_reduction': baseline_tf - min_tf,
        'log2_max_reduction': np.log2(baseline_tf / min_tf) if min_tf > 0 else np.inf
    }

    return patient_data, metrics


def define_response(tf_series, baseline, criteria='2log'):
    '''
    Define molecular response based on tumor fraction changes.

    Args:
        tf_series: Series of tumor fractions
        baseline: Baseline tumor fraction
        criteria: Response criteria (e.g., '2log' for 2-log reduction)
    '''
    if criteria == '2log':
        # 2-log (100-fold) reduction
        threshold = baseline / 100
    elif criteria == '1log':
        threshold = baseline / 10
    elif criteria == 'undetectable':
        threshold = 0.001  # Assay-dependent LOD

    response = tf_series < threshold
    return response