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.

Quality Metrics

"Assess quality of my IMC acquisition" → Evaluate IMC data quality through signal-to-noise ratios, channel correlations, tissue integrity scores, and acquisition-specific QC metrics.

• Python: numpy/scipy for SNR calculation and channel correlation analysis

Signal-to-Noise Ratio

import numpy as np
from scipy import ndimage
from skimage import io

def calculate_snr(image, mask=None):
    '''Calculate signal-to-noise ratio for an image channel.'''
    if mask is None:
        mask = image > np.percentile(image, 10)

    signal = np.mean(image[mask])
    noise = np.std(image[~mask])

    if noise == 0:
        return np.inf

    snr = signal / noise
    return snr

def calculate_snr_all_channels(image_stack, channel_names, tissue_mask=None):
    '''Calculate SNR for all channels in stack.'''
    results = {}
    for i, name in enumerate(channel_names):
        snr = calculate_snr(image_stack[i], tissue_mask)
        results[name] = snr
    return results

image_stack = io.imread('imc_image.tiff')
channel_names = ['CD45', 'CD3', 'CD68', 'panCK', 'DNA']
snr_values = calculate_snr_all_channels(image_stack, channel_names)

for ch, snr in snr_values.items():
    status = 'PASS' if snr > 3 else 'WARN' if snr > 1.5 else 'FAIL'
    print(f'{ch}: SNR = {snr:.2f} [{status}]')

Channel Correlation

def calculate_channel_correlation(image_stack, channel_names):
    '''Calculate pairwise correlation between channels.'''
    n_channels = image_stack.shape[0]
    flat_data = image_stack.reshape(n_channels, -1)

    corr_matrix = np.corrcoef(flat_data)

    import pandas as pd
    corr_df = pd.DataFrame(corr_matrix, index=channel_names, columns=channel_names)
    return corr_df

def flag_unexpected_correlations(corr_df, expected_pairs=None, threshold=0.7):
    '''Flag unexpected high correlations (possible spillover).'''
    issues = []

    if expected_pairs is None:
        expected_pairs = []

    for i, ch1 in enumerate(corr_df.columns):
        for j, ch2 in enumerate(corr_df.columns):
            if i >= j:
                continue

            corr = corr_df.loc[ch1, ch2]
            pair = (ch1, ch2)
            is_expected = pair in expected_pairs or (ch2, ch1) in expected_pairs

            if corr > threshold and not is_expected:
                issues.append({'channel_1': ch1, 'channel_2': ch2, 'correlation': corr, 'expected': is_expected})

    return pd.DataFrame(issues)

corr_matrix = calculate_channel_correlation(image_stack, channel_names)
print('Channel correlations:')
print(corr_matrix.round(2))

expected = [('CD3', 'CD45')]
issues = flag_unexpected_correlations(corr_matrix, expected)
if len(issues) > 0:
    print('\nUnexpected high correlations:')
    print(issues)