Imputing Proteomics Data
Impute missing values in protein-level proteomics data matrices. Use when (1) preparing proteomics data for downstream analyses requiring complete matrices (PCA, batch correction), (2) evaluating whether imputation is needed, (3) selecting appropriate imputation methods, or (4) assessing imputation quality. Does NOT cover normalization or batch correction.
Install
$ agentstack add skill-mannlabs-proteomics-agent-skills-imputing-proteomics-data ✓ 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.
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Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-mannlabs-proteomics-agent-skills-imputing-proteomics-data) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
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Imputing Proteomics Data
Impute missing values in protein intensity matrices for downstream analysis requiring complete data.
When to Impute
Impute when:
- Downstream analysis requires complete data (e.g. PCA, COMBAT batch correction)
- Missingness rate is moderate ( Random Forest > KNN > median imputation |
| MAR | BPCA > Random Forest > KNN > median imputation | | MNAR dominant (many low-abundance) | Density Probability Estimation (DPC/LIMPA) > MinProb > MinDet | | Datasets with many (ca. >500) samples | PIMMS (autoencoder) |
Quality Assessment
Evaluate imputation success:
- Distribution comparison: Imputed values should match the overall intensity distribution (not create artificial modes)
- PCA stability: Compare PCA before/after imputation using Procrustes analysis
- Covariance preservation: Frobenius norm between original and imputed covariance matrices
Red flags:
- Imputed values clustered at single point (MinDet/MinProb artifacts)
- Sample clustering changes dramatically after imputation
- Variance inflation in highly missing features
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.
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Versions
- v0.1.0 Imported from the upstream source.