Performing Statistical Analysis

Performing Proteomics Statistical Analysis

The goal of Differential Expression Analysis (DEA) is to identify proteins that change significantly between conditions.

Context

Experimental conditions

• Pairwise (2 Groups): Explicitly define "Control" vs. "Treatment"
• Multi-Group (>2 Groups):
  • Reference-based: Compare every condition against a universal control (e.g., Drug_A vs Ctrl, Drug_B vs Ctrl).
  • All-vs-All: Compare every permutation (e.g., Drug_A vs Drug_B).
  • Global: Use ANOVA to detect if any change exists across groups.

Statistical models

• t-test: The default for pairwise comparisons. With small sample sizes (typically n < 5 per group), use moderated t-tests (e.g., limma's eBayes), which borrow variance information across proteins to stabilize estimates.
• ANOVA: Required when comparing more than two groups simultaneously.
• Linear models: Recommended for complex designs. Use standard linear models for factorial (case-control) designs or batch correction; use linear mixed-effects models specifically when you have repeated measures (e.g., paired samples, time-courses) to account for within-subject correlation.

Multiple Testing Correction

Correcting the resulting p-values for multiple finding testing. The standard correction method to control false positives identifications is Benjamini-Hochberg FDR.

2. Workflow Statistical Testing

Copy this checklist and track progress:

Testcase Progress:
- [ ] Step 1: Define the experimental conditions for comparision
- [ ] Step 2: Run statistical model
- [ ] Step 3: Apply Multiple Testing Correction
- [ ] Step 4: Filter for regulated proteins
- [ ] Step 5: Create a Volcano plot
- [ ] Step 6: Create a Heatmap
- [ ] Step 7: Export results

Step 1: Define the experimental conditions for comparision

Check the study context (Experiment.md) and the prompt.

• For 2 Groups: Define group1 (Control) and group2 (Treatment).
• For >2 Groups: Select Strategy A (ANOVA for global differences) or Strategy B (Specific Pairwise Contrasts).

Step 2: Run a statistical model

• Pairwise: t-test. Input: Log2-transformed intensities.
• Multi-group global: One-way ANOVA. Output: Calculate log2_fold_change and raw p_value.

Step 3: Apply Multiple Testing Correction

Method: Apply Benjamini-Hochberg (BH) to raw p-values. Result: Generate a q_value (FDR-adjusted p-value) column.

Step 4: Filter for regulated proteins

Mark proteins as "Significant" (True/False) based on:

• q_value < 0.05 (5% FDR).
• abs(log2_fold_change) > 1 (2-fold change).

Step 5: Create a Volcano plot

Scatter plot of log2_fold_change (x-axis) vs. -Log10 q-value (y-axis). Color significant points.

Hints - Too many significant hits: The cutoff is too loose. Increase log2_fold_change threshold or use a stricter FDR (0.01).

Step 6: Create a Heatmap

• Data: Subset to "Significant" proteins.
• Scaling: Z-score normalize per feature (here: protein).
• Clustering: Hierarchical clustering on rows=samples and columns=proteins. Validates if replicates cluster together.

Step 7: Export results

Save table results_statistical_analysis.tsv with columns: protein_group, gene_name, log2_fold_change, p_value, -log10_pvalue, q_value, is_significant.