Quantitative Proteomics

Quantitative Proteomics — Reasoning Skill

Overview

You are an expert in quantitative proteomics experimental design and statistical analysis. When the user asks about quantification strategy, imputation, normalization, or differential expression for proteomics data, apply the decision frameworks below.

Usage

• Choose between LFQ, TMT, and DIA quantification strategies for a given study design
• Select imputation and normalization methods based on missingness patterns and data properties

Core Concepts

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Response Format

• Lead with the direct recommendation or classification (≤3 sentences)
• Structure as: recommendation → justification (citing specific criteria/thresholds) → caveats
• Use tables for comparisons; bullet points for criteria lists
• Omit background the user already knows — they asked the question
• Target: 200-400 words unless the user requests exhaustive detail

The decision trees and frameworks in this skill are for internal reasoning only. Apply them to reach your conclusion, but do not reproduce them in your response. Present only the final recommendation with supporting evidence.

1. Quantification Strategy Selection

1.1 Decision Tree

Is the goal discovery or targeted validation?
├─ Discovery (maximize coverage)
│  ├─ Sample count ≤ 20 → LFQ (label-free quantification)
│  ├─ Sample count 20–100 → TMT (multiplexed, up to 18-plex)
│  └─ Sample count any, need comprehensive coverage → DIA
├─ Targeted validation
│  └─ Use PRM (parallel reaction monitoring) or SRM/MRM
└─ Large clinical cohort (>100 samples)
   ├─ Budget allows multiplexing → TMT with fractionation
   └─ Budget constrained → DIA (single-shot)

1.2 Key Rules

1. Never mix quantification strategies within a single study unless you have a rigorous batch-correction plan.
2. LFQ requires match-between-runs (MBR) to reduce missingness — but MBR can introduce false transfers (~5% false positive rate at default settings).
3. DIA library-free mode (DIA-NN ≥1.8) is simpler; library-based gives ~10% more IDs.
4. Biological replicates matter more than technical replicates. Minimum 3 per condition; 5+ for clinical studies.
5. TMT ratio compression: Expect 30–50% underestimation at MS2 level. True FC ≈ observed FC × 1.5–2.0. SPS-MS3 reduces compression to 50% missing across ALL groups → Exclude protein (unreliable)

├─ Missing predominantly in one condition │ └─ Likely MNAR → Use left-censored methods (MinProb, QRILC) ├─ Missing scattered across conditions │ └─ Likely MAR → Use kNN, MLE, or BPCA └─ Mixed pattern └─ Hybrid: classify each protein's missingness, apply appropriate method

### 2.2 Imputation Methods and Pitfalls

| Method | Mechanism | When to Use | Pitfall |
|---|---|---|---|
| MinProb | MNAR | Low-abundance below LOD | Underestimates variance |
| QRILC | MNAR | Left-censored distributions | Assumes normality |
| kNN (k=10) | MAR | Scattered missingness | Fails if too many missing |
| MLE (EM) | MAR | Well-behaved MAR data | Assumes multivariate normality |
| BPCA | MAR | High-dimensional data | Computationally expensive |

### 2.3 Imputation Rules

1. **Always visualize missingness first.** Plot heatmap and density plots before choosing method.
2. **Filter before imputing.** Remove proteins with >50% missing and contaminants.
3. **MNAR parameters:** downshift = 1.8 SD, width = 0.3 SD from the observed distribution.
4. **Never impute then filter.** Filtering after imputation biases the dataset.
5. **Sensitivity analysis:** Run DE with and without imputation to confirm key hits are robust.

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## 3. Normalization Strategy

### 3.1 Decision Tree

What is the expected biological variation? ├─ Most proteins unchanged between conditions (typical) │ ├─ Default → Median centering (simple, robust) │ ├─ Strong batch effects → Quantile normalization │ └─ Variance depends on intensity → VSN ├─ Global shift expected (e.g., drug treatment affecting many proteins) │ └─ Use spike-in standards, NOT data-driven methods └─ TMT data └─ Sample loading normalization → then median centering within plex └─ DIA data └─ Median centering on precursor quantities BEFORE protein roll-up

### 3.2 Rules

1. **Log2-transform intensities before normalization** (except VSN, which includes transformation).
2. **Do NOT quantile-normalize if >30% of proteome is affected.** It will mask real biology.
3. **TMT requires two-step normalization:** sample loading normalization, then internal reference scaling.
4. **Batch correction (ComBat) is separate from normalization.** Normalize first, then correct.
5. **Check with box plots and MA plots.** Post-normalization medians should align.

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## 4. Differential Expression Interpretation

### 4.1 FC Thresholds and Rules

1. **FC thresholds depend on biology.** Secreted biomarkers: |log2FC| > 0.5. Intracellular: |log2FC| > 1.0.
2. **DEqMS is preferred over limma** because it models variance as a function of peptide count.
3. **Always use adjusted p-values.** Unadjusted p 4,000 (LFQ), >6,000 (DIA), >8,000 (TMT) |
| CV of intensities | 0.95 (technical), >0.85 (biological) |
| PCA clustering | Replicates cluster together |

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## 6. Critical Thresholds Quick Reference

- **CV > 20% across replicates** → flag run for investigation
- **Proteins >50% missing in ALL groups** → exclude before imputation
- **TMT ratio compression correction:** true FC ≈ observed FC × 1.5–2.0 (MS2 level)
- **Quantile normalization forbidden** if >30% of proteome differentially expressed
- **Phosphoproteomics:** require site localization probability >0.75; normalize phospho to total protein
- **Proteomics-transcriptomics correlation** is typically r = 0.4–0.6 — discordance is biologically informative (post-translational regulation)

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## 7. Quick-Reference Decision Summary

Quantification: Small discovery → LFQ Large cohort → TMT (with SPS-MS3) Comprehensive → DIA

Imputation: Low-abundance missing → MinProb / QRILC (MNAR) Random missing → kNN / MLE (MAR) Mixed → Hybrid approach

Normalization: Default → Median centering Batch effects → Quantile Heteroscedastic → VSN Global shift → Spike-in standards

DE Testing: Default → limma (empirical Bayes) Proteomics-aware → DEqMS Threshold → adj.p 1.0

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## Pipeline Reference

### Input Formats

| Source | File | Key Columns |
|---|---|---|
| MaxQuant | proteinGroups.txt | Protein IDs, Gene names, LFQ intensity *, iBAQ, Reverse, Potential contaminant |
| MSFragger | combined_protein.tsv | Protein, Gene, *Intensity*, *Spectral Count* |
| Proteome Discoverer | Proteins.txt | Accession, Description, Abundance * |
| DIA-NN | report.pg_matrix.tsv | Protein.Group, sample columns with quantities |

### TMT Sample Loading Normalization + Internal Reference Scaling

```r
# TMT: Sample Loading Normalization + Internal Reference Scaling
col_sums 2 groups | Pairwise t-tests inflate FDR | `limma::contrasts.fit()` with proper contrast matrix |
| Paired samples (e.g., tumor vs adjacent normal) | Must account for patient effect | Include patient as blocking factor in design matrix |

### Parameter Reference

| Parameter | Default | Range | Notes |
|---|---|---|---|
| MinProb quantile (q) | 0.01 | 0.001–0.05 | Lower = more conservative imputation |
| MinProb downshift (σ) | 1.8 | 1.4–2.0 | Standard deviations below mean |
| MinProb width | 0.3 | 0.2–0.5 | Fraction of observed σ for imputed spread |
| kNN k | 10 | 5–15 | Higher k = smoother but slower |
| kNN rowmax | 0.5 | 0.3–0.7 | Max fraction missing per row allowed |
| Missingness filter | 70% in ≥1 group | 50–100% | Stricter = fewer proteins, less noise |
| log2FC threshold | 1.0 | 0.5–2.0 | Adjust to biological context |
| adj.p threshold | 0.05 | 0.01–0.1 | 0.01 for stringent discovery |
| Unique peptides min | 2 | 1–3 | ≥2 required for confident ID |

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## When NOT to Use This Skill

- Validating biomarker candidates for clinical assay development (needs assay chemist)
- When sample prep issues dominate variance (pre-analytical problem, not analytical)
- Absolute quantification requiring isotope-labeled standards

## When to Escalate to a Human Expert

- When missing data exceeds 50% and imputation assumptions are untestable
- Before publishing quantitative claims from single-batch experiments
- When results require mass spectrometry method development expertise

## 8. Troubleshooting Common Issues

| Symptom | Likely Cause | Solution |
|---|---|---|
| Very few protein IDs (50%) | Normalization failure or global shift | Check box plots, consider spike-in normalization |
| Imputation creates artificial clusters | MNAR imputation too aggressive | Reduce downshift, try kNN, or filter more stringently |
| TMT fold changes smaller than expected | Ratio compression | Use SPS-MS3 or apply compression correction |
| High CV between replicates (>30%) | Sample prep variability | Review digestion protocol, add QC samples |

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [awslabs](https://github.com/awslabs)
- **Source:** [awslabs/hcls-agent-skills](https://github.com/awslabs/hcls-agent-skills)
- **License:** MIT-0

Install and usage instructions live in the source repository linked above.