tools-only Proteomics Pipeline Usage Guide
Overview
End-to-end workflow for label-free proteomics analysis from MaxQuant/DIA-NN output to differential protein abundance.
Prerequisites
BiocManager::install(c('limma', 'DEP', 'MSstats'))
install.packages(c('pheatmap', 'ggplot2'))
Quick Start
Tell your AI agent what you want to do:
- "Run the proteomics pipeline on my MaxQuant output"
- "Find differentially expressed proteins between conditions"
- "Process my DIA-NN results and run differential analysis"
Example Prompts
Basic Analysis
"I have proteinGroups.txt from MaxQuant, run the full pipeline"
"Normalize my proteomics data and find differential proteins"
QC and Preprocessing
"Check sample quality with PCA and correlation heatmap"
"Impute missing values in my proteomics data using MinProb"
Differential Analysis
"Run limma to find proteins changed between treatment and control"
"Use MSstats for differential analysis with my peptide-level data"
Pipeline Stages
1. Data Import
- Load proteinGroups.txt (MaxQuant) or report.tsv (DIA-NN)
- Filter contaminants and decoys
- Extract intensity columns
2. Transformation
- Replace 0 with NA
- Log2 transform
- Median centering normalization
3. Filtering
- Remove proteins with >50% missing values
- Filter low-variance proteins (optional)
4. Imputation
- MinProb: Left-censored Gaussian (for MNAR)
- KNN: K-nearest neighbors (for MAR)
- Perseus-style: Downshifted Gaussian
5. Quality Control
- PCA: Check replicate clustering
- Correlation heatmap: Sample similarity
- Missing value patterns: Random or systematic
6. Differential Analysis
- limma: Empirical Bayes moderated t-test
- MSstats: Mixed-effects models
- DEP: Complete workflow package
7. Output
- Differential proteins table
- Volcano plot
- Heatmap of significant proteins
Input Requirements
MaxQuant Output
proteinGroups.txt # Protein-level quantification
evidence.txt # Peptide-level (for MSstats)
annotation.csv # Sample metadata
Sample Annotation
sample,condition,replicate
Sample1,Control,1
Sample2,Control,2
Sample3,Treatment,1
Sample4,Treatment,2
Expected Outputs
| File | Description |
|---|---|
| differential_proteins.csv | All proteins with statistics |
| volcano_plot.pdf | Log2FC vs -log10(p-value) |
| pca_plot.pdf | Sample clustering |
| heatmap.pdf | Significant proteins |
Typical Results
- 2000-5000 quantified proteins (cell lysate)
- 50-500 differential proteins (10%)
- Fold changes typically 1.5-4x
Tips
- Missing values: Use MinProb for MNAR (abundance-dependent), KNN for MAR
- Normalization: Median centering is standard; quantile if distributions vary
- Filtering: Remove proteins with >50% missing values before imputation
- Replicates: Minimum 3 biological replicates per condition
- Contaminants: Always filter MaxQuant contaminants and reverse sequences