Version Compatibility

Reference examples tested with: MSnbase 2.28+, numpy 1.26+, pandas 2.2+

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

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

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

Protein Quantification

"Quantify proteins from my mass spec data" → Extract protein abundances from MS data using label-free (LFQ, spectral counting), isobaric labeling (TMT, iTRAQ), or metabolic labeling (SILAC) approaches.

• R: MSstats::dataProcess() for feature-to-protein summarization
• Python: pandas for MaxLFQ-style normalization and ratio calculation
• R: MSnbase for isobaric tag reporter ion extraction

Label-Free Quantification (LFQ)

Intensity-Based (MaxLFQ Algorithm)

import pandas as pd
import numpy as np

def maxlfq_normalize(intensities):
    '''Simplified MaxLFQ normalization'''
    log_int = np.log2(intensities.replace(0, np.nan))

    # Median centering per sample
    sample_medians = log_int.median(axis=0)
    global_median = sample_medians.median()
    normalized = log_int - sample_medians + global_median

    return normalized

Spectral Counting

def spectral_count_normalize(counts, total_spectra):
    '''Normalized spectral abundance factor (NSAF)'''
    # Divide by protein length, then by total
    nsaf = counts / total_spectra
    return nsaf / nsaf.sum()

TMT/iTRAQ Quantification

library(MSnbase)

# Load reporter ion data
tmt_data <- readMSnSet('tmt_data.txt')

# Normalize with reference channel
tmt_normalized <- normalize(tmt_data, method = 'center.median')

# Summarize to protein level
protein_data <- combineFeatures(tmt_normalized, groupBy = fData(tmt_data)$protein,
                                 fun = 'median')