normalizing-proteomics-data skill

Normalizing Proteomics Data

Normalization makes samples comparable by aligning their overall intensity distributions. It serves three main goals:

• Aligning intensities across samples, which removes technical variation caused by differences in sample loading amounts or instrument behavior.
• Preserving feature ranks within each sample, so the relative ordering of features is not distorted.
• Stabilizing variance (i.e., addressing heteroscedasticity), which most downstream methods require because they assume homoscedastic noise.

After normalization, a feature's intensity should reflect its relative biological abundance within a sample and remain comparable across samples.

Common normalization methods include:

| Priority | Method | Input scale | | --- | --- | --- | --- | | 1 | Total sum normalization | Simple offset correction; robust baseline | Linear-scale intensities | | 2 | Quantile Normalization | Distributions should be identical across samples | Log-transformed intensities | | 3 | Variance Stabilizing Normalization (VSN) | Need variance stabilization of low-abundant features; performs well in differential expression benchmarks | Linear-scale intensities | | 4 | LOESS/RLR | Suspect intensity-dependent bias (non-linear or linear) | Log-transformed intensities |

Recommendation: Start with log2 only. If PCA shows intensity-driven structure, try total sum normalization, then VSN.

Checklist

Copy this checklist and track progress:

Analysis step progress:
- [ ] Verify log2 transformation
- [ ] Assess Normalization Need
- [ ] Data dependent: Select Additional Normalization
- [ ] Evaluate Normalization Effect
      If Successful: Proceed to "Batch correction"
      If Unsuccessful: Return to Step 3 (Select normalization method 2, 3, etc.)

Workflow

1. Apply Log Transformation (Always Recommended)

Handle zeros as missing before log transformation

2. Assess Normalization Need

Check if total summed intensity drives variation:

• Visualize PCA colored by total intensity
• Compute correlation between PC1 and log10(total intensity per sample)
• If total intensity correlates strongly with major PCs, perform normalization attempts

3. Select Additional Normalization (if necessary)

When total intensity drives variance, try the normalization methods from the normalization method table in the order of their priority.

[!Important] Different normalization methods assume different data distributions. Ensure a priori, that the current data distribution meets the assumptions of the normalization method based on the column Input scale in the table. If you select a normalization method that assumes linear-scaled intensities, you need to go back to linear-scaled intensities and apply these normalization methods to the raw intensities, then log-transform the normalized data.

4. Evaluate Normalization Effect (Required)

It is critical that you assess the performance of the normalization, as benchmarks showed that many normalization methods perform worse than log2 alone on spike-in datasets and LFQ-normalized data (e.g., directLFQ) may not require additional normalization.

Pooled Median Absolute Deviation (PMAD) Average median absolute deviation across all features within a biological condition should drop

• Correlation of principal components with total intensity should drop.

• Pearson correlation between technical replicates should increase or stay stable