| name | performing-statistical-analysis |
| description | Perform differential expression analysis on preprocessed proteomics data. Use this when performing finding testing (t-tests, ANOVA) to identify regulated proteins, and multiple testing correction (FDR). Generates standard visualizations like Volcano plots. |
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.
