performing-statistical-analysis

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.