Convert raw mass spectrometry signals into protein abundance estimates using label-free, isobaric, or metabolic labeling strategies.
pip install numpy pandas scipy
# R packages: BiocManager::install(c("MSstats", "DEP", "MSnbase"))Tell your AI agent what you want to do:
- "Normalize my MaxLFQ intensities using median centering"
- "Process TMT reporter ion intensities from my experiment"
- "Impute missing values using KNN for my protein matrix"
"Apply median centering normalization to my protein intensity matrix"
"Use quantile normalization to correct for batch effects between runs"
"Normalize my TMT data using the internal reference channel"
"Impute missing values using KNN for MAR pattern and MinProb for MNAR"
"Filter proteins with more than 50% missing values, then impute the rest"
"Analyze the missing value pattern and recommend an imputation strategy"
"Calculate MaxLFQ intensities from peptide-level data"
"Summarize peptide intensities to protein level using top3 method"
"Extract TMT reporter ion intensities and correct for isotope impurity"
"Normalize across TMT plexes using the bridge channel"
- Load protein/peptide intensity matrix
- Log2-transform raw intensities
- Apply appropriate normalization method
- Identify missing value pattern (MCAR/MAR/MNAR)
- Impute missing values with suitable method
- Generate QC metrics (CV, correlation)
| Method | Description | Use Case |
|---|---|---|
| Median centering | Shift to common median | General purpose |
| Quantile | Force identical distributions | Strong batch effects |
| LOESS | Local regression | Non-linear effects |
| VSN | Variance stabilization | Heteroscedastic data |
| Type | Method |
|---|---|
| MCAR | Mean/median imputation |
| MAR | KNN imputation |
| MNAR (low abundance) | MinDet, MinProb, left-censored |
- Always log2-transform before normalization
- Check CV across replicates (technical <20%, biological <40%)
- Use PCA to verify normalization removed batch effects
- Document imputation method - it affects downstream statistics