Quality control for bottom-up proteomics across three levels -- instrument/raw-signal (mass accuracy, RT/iRT fit, FWHM, TIC vs injection time, % MS2 identified), identification/run (missed cleavages, charge states, PTM handling artifacts, contaminants), and experiment/quantitative (replicate correlation on log2, CV on the linear scale, completeness, MNAR-vs-MCAR missingness, PCA/batch, TMT channel balance, DIA q-values). Frames QC as a control chart against a per-instrument rolling baseline, not fixed cutoffs, and mandates inspecting raw boxplots, per-sample ID counts, total signal, and contaminant removal BEFORE normalizing -- because median normalization erases loading failures. Use when assessing proteomics data quality, diagnosing outlier samples, or deciding which samples to exclude before differential testing. The statistical test itself is differential-abundance; normalization mechanics are quantification; DIA q-value internals are dia-analysis.
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Quality control for bottom-up proteomics across three levels -- instrument/raw-signal (mass accuracy, RT/iRT fit, FWHM, TIC vs injection time, % MS2 identified), identification/run (missed cleavages, charge states, PTM handling artifacts, contaminants), and experiment/quantitative (replicate correlation on log2, CV on the linear scale, completeness, MNAR-vs-MCAR missingness, PCA/batch, TMT channel balance, DIA q-values). Frames QC as a control chart against a per-instrument rolling baseline, not fixed cutoffs, and mandates inspecting raw boxplots, per-sample ID counts, total signal, and contaminant removal BEFORE normalizing -- because median normalization erases loading failures. Use when assessing proteomics data quality, diagnosing outlier samples, or deciding which samples to exclude before differential testing. The statistical test itself is differential-abundance; normalization mechanics are quantification; DIA q-value internals are dia-analysis.
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.
Proteomics Quality Control -- A Three-Level Funnel Where the Matrix Sees the Failure Last
"Check the quality of my proteomics data" -> Read instrument, identification, and quantitative metrics as a descending funnel of silent failures, and inspect raw signal BEFORE normalizing -- because by the time a fault reaches the deliverable matrix, normalization has usually erased the evidence.
Python: pandas for matrix QC; matplotlib/seaborn for raw boxplots, correlation heatmaps, PCA
R: PTXQC createReport() for MaxQuant search-table QC; limma::plotMDS()/plotDensities(); MSstatsTMT dataProcessPlotsTMT() for TMT channel balance
Scope: This skill OWNS QC diagnosis across all three levels -- which metric localizes which fault, what threshold means trouble, and the mandatory inspect-before-normalize ordering. Normalization mechanics route to quantification; the differential test routes to differential-abundance; DIA q-value computation routes to dia-analysis. OUT OF SCOPE: running the statistical test, the normalization algorithms themselves, and DIA q-value/FDR internals.
The Single Most Important Modern Insight -- QC Is a Three-Level Funnel and Normalization Hides the Evidence
QC is a three-level funnel of silent failures, and the deliverable matrix is the LAST place a problem becomes visible. Faults originate at the instrument (spray, calibration, column) or in identification (digestion, contamination, PTM artifacts), but a protein matrix only shows the downstream symptom -- a low correlation or an outlier sample. A matrix-only QC pass is one-third of the job and blind to where faults actually start. Localize by descending: read every metric together with its co-readouts, never alone.
Almost no metric has a universal pass/fail cutoff; the defensible practice is a per-instrument, per-method control chart. Deviation from a lab's own rolling baseline (Levey-Jennings, +/-2 SD warn, +/-3 SD action) detects faults that a constant threshold misses or false-flags (Neely and Palmblad 2024). The numbers below seed a control chart, they are not standards-body limits.
Median normalization HIDES loading problems that must be SEEN first. Median/quantile normalization works by forcing a chosen summary statistic of every sample equal. A sample that genuinely loaded 3x low sits visibly shifted down in a RAW boxplot -- an obvious, diagnosable defect. The instant the matrix is median-normalized, the algorithm shifts that sample up by a constant to match everyone's median; boxplots line up perfectly; the evidence is mathematically erased. Worse, the low-loaded sample's noisy low-abundance signal gets stretched up to mid-range and injected into the differential test while QC plots look pristine. MANDATE: inspect raw/un-normalized boxplots plus per-sample ID counts, total signal, and missing fraction BEFORE normalizing; remove loading/injection failures and contaminants; THEN normalize and re-plot on the survivors. The identical principle governs TMT channel-loading balance.
The most integrative metric (% MS2 identified / ID count) is the first alarm but the LEAST specific -- it moves whenever anything upstream degrades. The same protein-count drop means spray (erratic TIC + maxed injection time), column (lost RT + broad peaks + rising backpressure), or sample (high contaminant fraction) depending on what co-moves.
Tool Taxonomy
Tool / method
Level
Citation
Mechanism / role
When
PTXQC (R, CRAN)
2+3
Bielow 2016
createReport() over MaxQuant txt/ or mzTab; per-metric scores in [0,1], QC heatmap PDF
proteinSummarization(), dataProcessPlotsTMT(); filters isolation interference on import
TMT channel-balance and QC plots
pandas / limma matrix QC
3
(this skill)
Correlation, CV, completeness, PCA on the matrix
Experiment-level QC (the code below)
differential-abundance
(route OUT)
--
The moderated test itself
Hit calling after QC passes
quantification
(route OUT)
--
Normalization and imputation mechanics
The how of normalizing
dia-analysis
(route OUT)
--
DIA q-value/FDR internals
DIA-NN/Spectronaut report computation
Decision Tree by Scenario
Scenario
Recommended
Why
MaxQuant txt/ folder, want fast multi-metric report
PTXQC createReport(txt_folder=...)
Scores Level-2/3 metrics vs a representative file; one PDF
Protein-count drop, cause unknown
Descend to Level 1: read TIC + injection time + RT/FWHM together
Co-readouts localize spray vs column vs sample
Replicate correlation low for one sample
Check if it correlates better with a DIFFERENT group
Distinguishes sample swap from prep failure
Boxplots flat but a sample feels wrong
Re-plot the RAW (un-normalized) matrix
Normalization erased the loading evidence
Deciding how to impute
Diagnose MNAR (left tail) vs MCAR (all-abundance) from the histogram FIRST
Wrong imputer corrupts present/absent calls
TMT data, channel looks off
MSstatsTMT QC plots on RAW reporter intensities
See the imbalance before global median rescales it
DIA matrix, how many proteins are real
Filter Global.Q.Value and Global.PG.Q.Value, route q internals to dia-analysis
Precursor q != protein q; both needed
Long sample queue, drift suspected
Interspersed QC every 4th-5th injection + Levey-Jennings
Turns one check into a time series
Default when uncertain: plot the RAW per-sample boxplots, ID counts, total signal, and missing fraction first; remove loading/injection failures and contaminants; only then normalize, re-plot, and proceed to correlation/CV/PCA on the survivors.
Inspect Raw Signal and Remove Contaminants Before Normalizing
Goal: Catch loading/injection failures and strip contaminant/decoy rows while they are still visible -- before normalization erases them.
Approach: Load the un-normalized matrix, plot per-sample boxplots plus ID counts and total signal, filter MaxQuant Potential contaminant/Reverse/Only identified by site rows, THEN log-transform and normalize on the survivors.
import pandas as pd
import numpy as np
contaminant_flags = ['Potential contaminant', 'Reverse', 'Only identified by site']
defstrip_contaminant_rows(protein_groups):
keep = pd.Series(True, index=protein_groups.index)
for col in contaminant_flags:
match = next((c for c in protein_groups.columns if c.lower() == col.lower()), None) # MaxQuant casing varies by version -- match case-insensitivelyifmatchisnotNone:
keep &= protein_groups[match].fillna('') != '+'# MaxQuant marks flagged rows with a literal '+'return protein_groups[keep]
defraw_sample_qc(raw_intensities):
return pd.DataFrame({
'n_quantified': raw_intensities.notna().sum(),
'total_signal': raw_intensities.sum(),
'median_intensity': raw_intensities.median(),
'missing_pct': 100 * raw_intensities.isna().sum() / len(raw_intensities)})
Read the boxplots before normalizing: a sample shifted >=2-3x below its group median is a loading/injection failure to exclude, not to rescale. The contaminant fraction of summed intensity should be small (PTXQC default flags >1%); keratin and trypsin autolysis dominate LOW-INPUT samples (single-cell, IPs, gel bands) because they are a roughly fixed absolute amount whose fractional share explodes as load shrinks.
Replicate Correlation on log2
Goal: Quantify reproducibility without letting a few abundant proteins fake agreement.
Approach: Correlate on log2 intensities (variance-stabilized, high-abundance tail compressed), report within-group pairs, and flag a sample correlating better with another group as a possible swap.
from itertools import combinations
defreplicate_correlation(log2_intensities, sample_groups):
corr = log2_intensities.corr(method='pearson') # log2 first: Pearson on raw is a high-abundance artifact
rows = []
for group in sample_groups.unique():
members = sample_groups[sample_groups == group].index
for s1, s2 in combinations(members, 2):
rows.append({'group': group, 's1': s1, 's2': s2, 'r': corr.loc[s1, s2]})
return pd.DataFrame(rows)
Technical replicates r > 0.98 (instrument noise only); biological r ~ 0.90-0.98 (genuine variance, lower is expected and correct); soft floor r > 0.8 to retain a biological replicate. A Spearman check is a robustness aid only -- ranks discard the magnitude that quant QC cares about.
Coefficient of Variation on the Linear Scale
Goal: Summarize per-condition precision with a number that means what it says.
Approach: Compute CV = SD/mean on LINEAR (non-log) intensities; if only logged values exist use the geometric-CV formula. Report the median CV per condition (the per-protein distribution is right-skewed).
defmedian_cv_linear(linear_intensities, sample_groups):
rows = []
for group in sample_groups.unique():
block = linear_intensities[sample_groups[sample_groups == group].index]
per_protein_cv = block.std(axis=1) / block.mean(axis=1) # base CV formula REQUIRES linear scale
rows.append({'group': group, 'median_cv_pct': 100 * per_protein_cv.median()})
return pd.DataFrame(rows)
defgeometric_cv_from_log(log_intensities):
sigma = log_intensities.std(axis=1) * np.log(2) # convert log2 SD to natural-log SDreturn100 * np.sqrt(np.expm1(sigma ** 2)) # gCV = sqrt(exp(sigma^2) - 1)
Applying the base formula to log-transformed data compresses CV ~14x (most proteins appear to have CV < 1%) -- meaningless (Brenes 2024). State normalization state, transform, and software params or the CV is uninterpretable: DIA-NN "High precision" mode silently median-normalizes, halving median CV vs "High accuracy". Technical median CV < ~10-20%, biological ~20-40%; a LOWER CV is not automatically better (loose FDR or faulty MS1 extraction produce artificially low CVs).
Missingness Mechanism and Completeness
Goal: Decide how to impute by first deciding why values are missing.
Approach: Diagnose the missingness profile -- left-tail concentration means MNAR (left-censored, abundance-dependent), all-abundance scatter means MCAR -- and filter on completeness before imputing only the shallow remainder.
defmissingness_profile(log2_intensities, n_bins=20):
observed = log2_intensities.stack()
abundance_bins = pd.qcut(observed, n_bins, duplicates='drop')
present_per_protein = log2_intensities.notna().mean(axis=1)
mean_abundance = log2_intensities.mean(axis=1)
return mean_abundance, present_per_protein # plot present-fraction vs abundance: rising-with-abundance = MNARdefcompleteness_filter(log2_intensities, sample_groups, min_valid_frac=0.7):
keep = pd.Series(False, index=log2_intensities.index)
for group in sample_groups.unique():
block = log2_intensities[sample_groups[sample_groups == group].index]
keep |= block.notna().mean(axis=1) >= min_valid_frac # valid in >=70% of >=1 conditionreturn log2_intensities[keep]
kNN-imputing a genuinely-absent (MNAR) value invents mid-range abundance and KILLS a real present/absent difference; a left-shifted draw (Perseus down-shifted normal, downshift=1.8 SD below the observed mean, width=0.3 of observed SD) on an MCAR gap FABRICATES a false low and inflates a difference. Match the imputer to the mechanism. The imputation mechanics themselves are quantification.
PCA and Batch Detection
Goal: See whether the dominant variance is biology or batch, and flag outlier samples.
Approach: On the normalized survivors, run PCA, color by condition and by batch, and test whether top PCs associate with batch.
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from scipy.stats import f_oneway
defpca_batch_check(normalized_log2, sample_info, batch_col='batch'):
imputed = normalized_log2.apply(lambda r: r.fillna(r.median()), axis=1) # temporary, for PCA only
pcs = PCA(n_components=5).fit(StandardScaler().fit_transform(imputed.T))
coords = pd.DataFrame(pcs.transform(StandardScaler().fit_transform(imputed.T)),
columns=[f'PC{i+1}'for i inrange(5)], index=normalized_log2.columns).join(sample_info)
for pc in ['PC1', 'PC2', 'PC3']:
groups = [coords[coords[batch_col] == b][pc] for b in coords[batch_col].unique()]
_, p = f_oneway(*groups)
print(f'{pc} ~ {batch_col}: p={p:.4f}')
return coords, pcs.explained_variance_ratio_
A sample isolated from its group is a removal/re-run candidate. If batch is PC1, correct it explicitly (ComBat, or include batch in the design matrix downstream) and re-inspect; never let batch be the dominant axis going into differential testing. Visualization of the projection routes to data-visualization/dimensionality-reduction-plots.
Per-Method Failure Modes
Median normalization hides loading failures
Trigger: Normalizing the matrix before inspecting raw per-sample signal. Mechanism: median-centering shifts each sample by a constant to equalize the very statistic that was the symptom of a low load. Symptom: flat, clean boxplots that hide a 3x-low sample now stretched into mid-range. Fix: plot RAW boxplots + ID counts + total signal first; exclude failures; then normalize.
Normalizing with contaminants still in the matrix
Trigger: Contaminant/decoy rows left in before log + normalize. Mechanism: keratin/trypsin/albumin inflate the denominator and shift the median; when their load differs across groups the differential gets normalized into the real proteins. Symptom: spurious fold changes; a contaminant fraction that varies by group. Fix: filter Potential contaminant + Reverse + Only identified by site BEFORE log + normalize.
Wrong imputer for the missingness mechanism
Trigger: kNN on MNAR, or left-shift on MCAR. Mechanism: kNN borrows mid-range neighbors for a value that is low because it is absent; left-shift draws a deep low for a value missing at random. Symptom: killed present/absent calls (kNN-on-MNAR) or inflated false lows (left-shift-on-MCAR). Fix: diagnose left-tail vs all-abundance from the histogram first; mechanics route to quantification.
CV computed on log-transformed data
Trigger: Base CV formula applied after log2. Mechanism: SD/mean is defined for linear intensity; logging compresses it ~14x. Symptom: most proteins appear to have CV < 1%. Fix: compute on linear intensity, or use the geometric-CV formula on logged values; always state transform + normalization + software.
Pearson r on raw intensity
Trigger: Correlating un-logged intensities. Mechanism: a few high-abundance proteins dominate the covariance. Symptom: r = 0.99 while the bulk disagrees. Fix: log2 before correlating; Spearman as a robustness check only.
Bimodal mass-error histogram read as calibration
Trigger: A two-peaked ppm-error distribution. Mechanism: almost always monoisotopic mis-assignment or co-isolation (a search/sample problem), NOT calibration drift; a generous tolerance still IDs the mis-assigned precursors so ID rate looks fine. Symptom: bimodal histogram, normal ID rate. Fix: correct isotope-error tolerance/deisotoping, not recalibration.
Charge-state distribution off the platform baseline
Trigger: The fully-tryptic 2+ fraction drifts from the rolling baseline. Mechanism: a tryptic peptide carries two basic sites (C-terminal K/R + N-terminus) so 2+ dominates; excess 3+/4+ comes from internal basic residues left by missed cleavages, excess 1+ from poor ionization, short peptides, or contaminants. Symptom: raised high-charge fraction (a digestion/chemistry signal) or raised 1+ fraction (an ionization/spray signal). Fix: read the charge distribution together with the missed-cleavage rate (high charge co-moving with missed cleavages = under-digestion) to separate a chemistry problem from a spray problem a raw protein-count drop cannot resolve alone.
TMT ratio compression from co-isolation
Trigger: Isobaric quant with a wide isolation window. Mechanism: near-isobaric co-eluting precursors are co-isolated and add their own reporters across all channels, a uniform pedestal. Symptom: every fold-change compressed toward 1 (a real 10:1 reads ~5:1). Fix: filter isolation interference < 50%; prefer SPS-MS3 (McAlister 2014) / FAIMS / narrow windows; run a TKO or empty-channel control to measure the floor.
Quantitative Thresholds
Threshold
Source
Rationale
Mass accuracy (internal/lock) median |err| < 1-3 ppm, single-mode centered 0
CONVENTION
width approaching MS1 tolerance loses real IDs
iRT RT-fit R^2 > 0.99 (warn below)
CONVENTION (mechanism firm)
residual is just LC noise on a stable gradient
FWHM alarm on > 20-30% rise vs baseline; >= 8-10 points across FWHM (floor ~5)
Kocher 2011
peak capacity tracks peptide IDs; points needed for accurate AUC
~95/99.7% of points under stable normal; catch drift early
Common Errors
Error / symptom
Cause
Solution
KeyError: 'Mass Error [ppm]'
MaxQuant column casing varies by version
match case-insensitively (Mass error vs Mass Error); never hard-code
Contaminant rows have True/False, filter keeps all
MaxQuant flags with a literal '+', not a boolean
filter col != '+'
CV unexpectedly tiny (< 1%)
base CV formula applied to log2 data
compute on linear intensity or use geometric CV
r = 0.99 but samples clearly differ
Pearson on raw (un-logged) intensity
log2 transform before correlating
PCA dominated by injection day
batch effect, not biology
correct (ComBat / batch in design) and re-inspect; do not proceed
PTXQC "not found" via BiocManager
PTXQC is on CRAN, not Bioconductor
install.packages('PTXQC')
createReport() errors on a dataframe arg
it takes a txt-folder path / mzTab / YAML, not dataframes
pass txt_folder= (the MaxQuant txt/ directory)
References
Bielow C, Mastrobuoni G, Kempa S. Proteomics Quality Control: Quality Control Software for MaxQuant Results. J Proteome Res 2016;15(3):777-787.
Kovalchik KA, Colborne S, Spencer SEP, et al. RawTools: Rapid and Dynamic Interrogation of Orbitrap Data Files for Mass Spectrometer System Management. J Proteome Res 2019;18(2):700-708.
Morgenstern D, Barzilay R, Levin Y. RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool. J Proteome Res 2021;20(4):2098-2104.
Kockmann T, Panse C. The rawrr R Package: Direct Access to Orbitrap Data and Beyond. J Proteome Res 2021;20(4):2028-2034.
Trachsel C, Panse C, Kockmann T, et al. rawDiag: An R Package Supporting Rational LC-MS Method Optimization for Bottom-up Proteomics. J Proteome Res 2018;17(8):2908-2914.
Ma ZQ, Polzin KO, Dasari S, et al. QuaMeter: Multivendor Performance Metrics for LC-MS/MS Proteomics Instrumentation. Anal Chem 2012;84(14):5845-5850.
Bereman MS, Beri J, Sharma V, et al. An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments. J Proteome Res 2016;15(12):4763-4769.
Kocher T, Swart R, Mechtler K. Ultra-High-Pressure RPLC Hyphenated to an LTQ-Orbitrap Velos Reveals a Linear Relation between Peak Capacity and Number of Identified Peptides. Anal Chem 2011;83(7):2699-2704.
Tyanova S, Temu T, Sinitcyn P, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016;13(9):731-740.
Brenes AJ. Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics. J Proteome Res 2024;23(12):5274-5278.
McAlister GC, Nusinow DP, Jedrychowski MP, et al. MultiNotch MS3 Enables Accurate, Sensitive, and Multiplexed Detection of Differential Expression across Cancer Cell Line Proteomes. Anal Chem 2014;86(14):7150-7158.
Huang T, Choi M, Tzouros M, et al. MSstatsTMT: Statistical Detection of Differentially Abundant Proteins in Experiments with Isobaric Labeling and Multiple Mixtures. Mol Cell Proteomics 2020;19(10):1706-1723.
Neely BA, Palmblad M, et al. Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer. J Biomol Tech 2024;35(3).
Related Skills
data-import - Load search-engine output and intensity matrices before QC
quantification - Normalization and imputation mechanics that QC mandates running AFTER inspection
differential-abundance - The moderated statistical test QC gates
dia-analysis - DIA q-value/FDR internals behind the protein-count QC
