Protein Quantification
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')
Python TMT Processing
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
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)
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
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