Performing Statistical Analysis
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.
Install
$ agentstack add skill-mannlabs-proteomics-agent-skills-performing-statistical-analysis ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-mannlabs-proteomics-agent-skills-performing-statistical-analysis) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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 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 log2foldchange 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 1 (2-fold change).
Step 5: Create a Volcano plot
Scatter plot of log2foldchange (x-axis) vs. -Log10 q-value (y-axis). Color significant points.
Hints - Too many significant hits: The cutoff is too loose. Increase log2foldchange 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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: MannLabs
- Source: MannLabs/proteomics-agent-skills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.