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)

python
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

python
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

r
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')

Python TMT Processing

python
def extract_tmt_intensities(spectrum, reporter_mz, tolerance=0.003):
    '''Extract TMT reporter ion intensities'''
    mz, intensity = spectrum.get_peaks()
    tmt_intensities = {}

    for channel, target_mz in reporter_mz.items():
        mask = np.abs(mz - target_mz) < tolerance
        if mask.any():
            tmt_intensities[channel] = intensity[mask].max()
        else:
            tmt_intensities[channel] = 0

    return tmt_intensities

TMT_10PLEX = {'126': 126.127726, '127N': 127.124761, '127C': 127.131081,
              '128N': 128.128116, '128C': 128.134436, '129N': 129.131471,
              '129C': 129.137790, '130N': 130.134825, '130C': 130.141145,
              '131': 131.138180}

SILAC Quantification

python
def calculate_silac_ratio(heavy_intensity, light_intensity):
    '''Calculate SILAC H/L ratio'''
    if light_intensity > 0 and heavy_intensity > 0:
        return np.log2(heavy_intensity / light_intensity)
    return np.nan

# Typical mass shifts
SILAC_SHIFTS = {
    'Arg10': 10.008269,  # 13C6 15N4 Arginine
    'Lys8': 8.014199,    # 13C6 15N2 Lysine
    'Arg6': 6.020129,    # 13C6 Arginine
    'Lys6': 6.020129     # 13C6 Lysine
}

MSstats Workflow (R)

Goal: Convert MaxQuant output into normalized protein-level abundance estimates using MSstats feature-to-protein summarization.

Approach: Reformat MaxQuant evidence and proteinGroups files into MSstats input format, then apply median equalization normalization with Tukey's median polish for protein-level summarization.

r
library(MSstats)

# Prepare input from MaxQuant
maxquant_input <- MaxQtoMSstatsFormat(
    evidence = read.table('evidence.txt', sep = '\t', header = TRUE),
    proteinGroups = read.table('proteinGroups.txt', sep = '\t', header = TRUE),
    annotation = read.csv('annotation.csv')
)

# Process and normalize
processed <- dataProcess(maxquant_input, normalization = 'equalizeMedians',
                         summaryMethod = 'TMP', censoredInt = 'NA')

# Protein-level summary
protein_summary <- quantification(processed)

Related Skills

• data-import - Load MS data before quantification
• differential-abundance - Statistical testing after quantification
• expression-matrix/counts-ingest - Similar matrix handling