# 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.

- **Type:** Skill
- **Install:** `agentstack add skill-mannlabs-proteomics-agent-skills-imputing-proteomics-data`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [MannLabs](https://agentstack.voostack.com/s/mannlabs)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [MannLabs](https://github.com/MannLabs)
- **Source:** https://github.com/MannLabs/proteomics-agent-skills/tree/main/plugins/proteomics/skills/imputing_proteomics_data

## Install

```sh
agentstack add skill-mannlabs-proteomics-agent-skills-imputing-proteomics-data
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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:

1. **Distribution comparison**: Imputed values should match the overall intensity distribution (not create artificial modes)
2. **PCA stability**: Compare PCA before/after imputation using Procrustes analysis
3. **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](https://github.com/MannLabs)
- **Source:** [MannLabs/proteomics-agent-skills](https://github.com/MannLabs/proteomics-agent-skills)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-mannlabs-proteomics-agent-skills-imputing-proteomics-data
- Seller: https://agentstack.voostack.com/s/mannlabs
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.