Version Compatibility

Reference examples tested with: pyOpenMS 3.1+, pandas 2.2+, numpy 1.26+, MSnbase 2.28+

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.

Mass Spectrometry Data Import -- Inheriting the Acquisition Contract and Stripping the Bookkeeping

"Load my mass spec data into Python" -> Parse spectra or a search-engine table AND immediately enforce two contracts -- which quant column carries real biology, and which rows are search-engine bookkeeping that must be deleted -- because the same proteinGroups.txt yields different conclusions depending on the column read and the rows kept.

• Python: pyopenms.MzMLFile().load(path, exp) for raw spectra; pandas.readcsv(sep='\t') for MaxQuant; pandas.readparquet for DIA-NN
• R: Spectra::Spectra() / QFeatures::readQFeatures() for raw and quantified data (MSnbase still works but is in maintenance mode)

Scope: this skill owns reading spectra/search outputs into memory, deleting decoy/contaminant/site-only rows, picking the correct quant column, and characterizing missingness. Format conversion (RAW -> mzML) -> peptide-identification. MaxLFQ/TMT reporter quant computation -> quantification. Protein-group parsimony -> protein-inference. Normalization and imputation -> differential-abundance and expression-matrix/normalization. OUT OF SCOPE: statistical testing, batch correction, and the actual imputation step (this skill only diagnoses the missingness so the right imputer is chosen later).

The Single Most Important Modern Insight -- Import Is Where Two Contracts Are Read and Enforced

1. A "data import" is never just file parsing -- it is the moment the acquisition mode's quantitative contract and its missingness structure are inherited. DDA selects the top-N most intense precursors per cycle, and which precursors get picked is partly stochastic and abundance-biased, so the same low-abundance peptide is sampled in run A and missed in run B; this manufactures structured, left-censored MNAR missingness. DIA fragments every precursor in every window every cycle, so its (fewer) missing values are closer to MCAR. The catastrophic error this prevents: imputing a DDA matrix with a mean/KNN method that assumes MCAR, which biases low-abundance proteins upward and manufactures false hits. The mode is born at acquisition and inherited at import; the missingness diagnosis made here dictates which imputation is even legitimate downstream.

1. The search engine's bookkeeping must be stripped before any number is trusted. A proteinGroups.txt carries decoy rows (Reverse == '+', REV prefix in the ID) from the target-decoy FDR machinery, contaminant rows (Potential contaminant == '+', CON prefix), and Only-identified-by-site rows (the protein has no unmodified-peptide evidence, only a modified site). Keeping any of these leaks non-biological signal into the intensity matrix and inflates IDs. The catastrophic error: reporting differential abundance on a matrix where decoy or keratin rows survived.

1. The same proteinGroups.txt yields different biology from different columns, and a zero is not a measurement. Intensity is raw summed precursor signal (not normalized, not comparable across samples for ratios). LFQ intensity is MaxLFQ-normalized and is the column for between-sample comparison. iBAQ is intensity divided by the number of observable tryptic peptides -- a within-sample molar proxy, not a between-sample quant. MaxQuant writes 0 for "not quantified", so log2(0) = -inf; replace 0 -> NaN before any transform. The catastrophic error: log2-transforming raw Intensity (or iBAQ) and reading the ratios as biology.

Tool Taxonomy

Decision Tree by Scenario

Default when uncertain: read LFQ intensity (MaxQuant) or PG.MaxLFQ after q-filtering (DIA-NN), strip Reverse/contaminant/site-only rows, set 0 -> NaN, then diagnose missingness before choosing an imputer.

Loading mzML/mzXML with pyOpenMS

Goal: Parse raw spectra into memory for QC, peak access, and isolation-window reasoning.

Approach: Load into an MSExperiment (filled in place), iterate by MS level; get_peaks() returns a tuple of (mz, intensity) numpy arrays, and getPrecursors() returns a list.

from pyopenms import MSExperiment, MzMLFile

exp = MSExperiment()
MzMLFile().load('sample.mzML', exp)  # fills exp in place; returns None

for spectrum in exp:
    if spectrum.getMSLevel() == 1:
        mz, intensity = spectrum.get_peaks()  # tuple of two numpy arrays
    elif spectrum.getMSLevel() == 2:
        precursor = spectrum.getPrecursors()[0]  # getPrecursors returns a list
        precursor_mz = precursor.getMZ()
        window = precursor.getIsolationWindowLowerOffset() + precursor.getIsolationWindowUpperOffset()

Loading and Cleaning MaxQuant proteinGroups.txt

Goal: Get a trustworthy log2 intensity matrix with bookkeeping rows removed and missing values represented as NaN.

Approach: Strip Reverse/contaminant/site-only rows, resolve the semicolon protein-ID list to a leading ID, pick LFQ intensity columns, set 0 -> NaN, then log2-transform.

import pandas as pd
import numpy as np

pg = pd.read_csv('proteinGroups.txt', sep='\t', low_memory=False)  # mixed-type cols

# Flag columns hold '+' or empty string; all three are proteinGroups-only bookkeeping
mask = (pg.get('Reverse', '') != '+') & (pg.get('Potential contaminant', '') != '+') & (pg.get('Only identified by site', '') != '+')
pg = pg[mask].copy()

# Protein IDs / Majority protein IDs / Gene names are SEMICOLON lists; take the first (leading/razor) entry
pg['leading_protein'] = pg['Protein IDs'].str.split(';').str[0]
pg['leading_gene'] = pg['Gene names'].where(pg['Gene names'].notna(), '').str.split(';').str[0]

lfq_cols = [c for c in pg.columns if c.startswith('LFQ intensity ')]  # MaxLFQ-normalized, between-sample comparable
matrix = pg[['leading_protein', 'leading_gene'] + lfq_cols].copy()
matrix[lfq_cols] = matrix[lfq_cols].replace(0, np.nan)  # MaxQuant writes 0 for missing; log2(0) = -inf
matrix[lfq_cols] = np.log2(matrix[lfq_cols])

Loading DIA-NN report.parquet

Goal: Reshape the long DIA-NN report into a confident protein-by-run matrix.

Approach: Read the parquet (default since 1.9, only default in 2.0), filter precursor- AND protein-group q-values to 1% FDR BEFORE pivoting on PG.MaxLFQ.

import pandas as pd

report = pd.read_parquet('report.parquet')  # report.tsv dropped as default in DIA-NN 2.0
report = report[(report['Q.Value'] <= 0.01) & (report['PG.Q.Value'] <= 0.01)]  # 1% FDR before quant

matrix = report.pivot_table(index='Protein.Group', columns='Run', values='PG.MaxLFQ', aggfunc='first')

Diagnosing the Missingness Contract

Goal: Quantify the missing-value pattern so the legitimate imputation class can be chosen downstream.

Approach: Count NaN per protein and per sample; relate the pattern to acquisition mode (DDA -> structured MNAR; DIA -> closer to MCAR). A correlation between missingness and mean abundance is the MNAR signature.

import numpy as np

def assess_missingness(matrix, sample_cols):
    miss_per_protein = matrix[sample_cols].isna().sum(axis=1)
    miss_per_sample = matrix[sample_cols].isna().sum(axis=0)
    total_pct = 100 * matrix[sample_cols].isna().sum().sum() / matrix[sample_cols].size
    mean_abund = matrix[sample_cols].mean(axis=1)  # negative corr with missingness => MNAR / left-censored
    mnar_corr = mean_abund.corr(miss_per_protein)
    return {'per_protein': miss_per_protein, 'per_sample': miss_per_sample, 'total_pct': total_pct, 'abundance_missing_corr': mnar_corr}

Per-Method Failure Modes

MaxQuant wrong quant column

Trigger: Reading Intensity (raw) or iBAQ when between-sample ratios are intended. Mechanism: Intensity is un-normalized summed precursor signal; iBAQ is a within-sample molar proxy. Neither is comparable across samples the way LFQ intensity is. Symptom: Ratios track total loaded protein / sample depth rather than biology; fold changes shift when one sample's loading changes. Fix: Use LFQ intensity for cross-sample comparison; if computing custom normalization use Intensity and normalize explicitly (expression-matrix/normalization).

Zero treated as a measurement

Trigger: np.log2 applied directly to a MaxQuant matrix still containing 0. Mechanism: MaxQuant encodes "not quantified" as 0; log2(0) = -inf, which then propagates into means and tests. Symptom: -inf values, NaN means, proteins silently dropped or skewed. Fix: replace(0, np.nan) before any transform; then diagnose missingness.

Bookkeeping rows survive

Trigger: Loading proteinGroups.txt without filtering Reverse / Potential contaminant / Only identified by site. Mechanism: Decoys exist only for FDR estimation; contaminants are keratin/trypsin/BSA, not the sample; site-only groups have no unmodified-peptide quant evidence. Only identified by site exists only in proteinGroups.txt. Symptom: Inflated protein counts; a "hit" that is a decoy or keratin. Fix: Filter all three flag columns; cross-check with REV/CON ID prefixes when joining to peptide tables. Caveat: do not delete CON__ rows blindly if a contaminant (e.g. keratin) is the protein of interest.

Razor / leading protein-ID ambiguity ignored

Trigger: Treating Protein IDs or Gene names as an atomic single value. Mechanism: These are semicolon-delimited lists; the first entry is the leading (razor) protein for the group, and Gene names can be blank while protein IDs are present. Symptom: Merges fail, NaN gene labels, ambiguous identity downstream. Fix: Split on ; and take the first entry; guard Gene names with .notna(). Group parsimony details -> protein-inference.

Stale DIA-NN parsing

Trigger: Reading report.tsv on DIA-NN 2.0, or pivoting before q-filtering. Mechanism: 2.0 defaults to (and only defaults to) report.parquet; pivoting unfiltered rows includes precursors above 1% FDR. Symptom: FileNotFoundError on report.tsv; or low-confidence quant inflating the matrix. Fix: pd.read_parquet('report.parquet'); filter Q.Value <= 0.01 & PG.Q.Value <= 0.01 before pivoting PG.MaxLFQ.

MNAR imputed as MCAR

Trigger: Mean/median/KNN imputation on a DDA matrix. Mechanism: DDA missingness is abundance-dependent (left-censored); MCAR imputers fill missing low values with the central tendency, biasing them upward. Symptom: Low-abundance proteins gain false high values; spurious differential hits. Fix: Diagnose the abundance-missingness correlation here; route DDA to left-censored imputation (downshifted-Gaussian / QRILC / MinProb) in differential-abundance; DIA tolerates standard imputers.

Quantitative Thresholds

Common Errors

References

• Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. 2014. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 13(9):2513-2526.
• Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. 2020. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 17(1):41-44.
• Chambers MC, Maclean B, Burke R, et al. 2012. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30(10):918-920.
• Hulstaert N, Shofstahl J, Sachsenberg T, et al. 2020. ThermoRawFileParser: modular, scalable, and cross-platform RAW file conversion. J Proteome Res 19(1):537-542.

Related Skills

• peptide-identification - search raw spectra and convert vendor RAW to mzML
• quantification - compute MaxLFQ and TMT reporter-ion quantities from imported data
• protein-inference - resolve protein-group parsimony and razor assignment
• differential-abundance - normalize, impute (per the missingness diagnosis), and test
• proteomics-qc - assess run-level identification and quant quality
• dia-analysis - run DIA-NN to produce the report this skill imports
• expression-matrix/normalization - general intensity-matrix normalization patterns
• workflows/proteomics-pipeline - end-to-end pipeline that begins with this import step