# Proteomics



import { AgentHero } from '@/components/AgentHero';
import { NextSteps } from '@/components/NextSteps';
import { Rocket, GraduationCap, Database } from 'lucide-react';

<AgentHero
  name="lobster-proteomics"
  tier="free"
  problem="Mass spectrometry analysis: protein quantification, differential abundance, and PTM detection"
  inputs={["MaxQuant", "DIA-NN", "Spectronaut", "FragPipe", "CSV"]}
  outputs={["Protein Abundances", "DE Proteins", "PTM Sites", "Volcano Plots"]}
  install="pip install lobster-proteomics"
  difficulty="intermediate"
  agents={[{
  name: "proteomics_expert",
  role: "MS data analysis and protein quantification",
  children: [
    { name: "proteomics_de_analysis_expert", role: "Differential expression analysis" },
    { name: "biomarker_discovery_expert", role: "Biomarker discovery and survival analysis" }
  ]
}]}
/>

Agents [#agents]

proteomics_expert [#proteomics_expert]

Parent orchestrator agent for proteomics analysis. Handles QC, preprocessing, and normalization directly, while delegating downstream analysis to specialized sub-agents.

**Capabilities:**

* MS data loading and preprocessing
* Protein quantification (LFQ, TMT, iTRAQ)
* Platform auto-detection (mass spectrometry vs affinity)
* Quality control with platform-specific metrics
* Normalization (median/log2 for MS, quantile for affinity)
* Pattern analysis with PCA and clustering
* Delegates DE analysis to proteomics\_de\_analysis\_expert
* Delegates biomarker discovery to biomarker\_discovery\_expert

proteomics_de_analysis_expert [#proteomics_de_analysis_expert]

Sub-agent for differential expression analysis, invoked by proteomics\_expert.

**Capabilities:**

* Differential protein expression between groups (7 statistical methods)
* Time course analysis for longitudinal studies
* Protein-target correlation analysis (Pearson, Spearman, Kendall)
* Platform-aware fold change thresholds (1.5x MS, 1.2x affinity)
* Multiple testing correction (Benjamini-Hochberg, Bonferroni)

biomarker_discovery_expert [#biomarker_discovery_expert]

Sub-agent for network analysis and survival-based biomarker identification, invoked by proteomics\_expert.

**Capabilities:**

* WGCNA-style co-expression network analysis
* Module eigengene computation and trait correlation
* Cox proportional hazards regression
* Kaplan-Meier survival analysis with log-rank tests
* Biomarker candidate ranking

Example Workflows [#example-workflows]

Differential Protein Analysis [#differential-protein-analysis]

```text
User: Analyze my proteomics data and find differentially
      abundant proteins between conditions

[proteomics_expert → proteomics_de_analysis_expert]
- Loads MS quantification data (MaxQuant, DIA-NN)
- Normalizes protein abundances
- Delegates to proteomics_de_analysis_expert for differential analysis
- Generates volcano plot
- Reports top differentially abundant proteins
```

PTM Analysis [#ptm-analysis]

```text
User: Identify phosphorylation changes in my dataset

[proteomics_expert]
- Filters for phosphopeptides
- Normalizes phosphosite intensities
- Calculates differential phosphorylation
- Maps sites to protein domains
- Reports kinase motif enrichment
```

Protein Identification [#protein-identification]

```text
User: What proteins were identified in my MS run?

[proteomics_expert]
- Summarizes identified proteins
- Reports coverage and peptide counts
- Shows intensity distribution
- Highlights high-confidence identifications
```

Multi-Omics Integration [#multi-omics-integration]

proteomics\_expert can integrate with transcriptomics data:

```text
User: Compare protein and mRNA levels for my samples

[proteomics_expert + transcriptomics_expert]
- Matches proteins to transcripts
- Correlates protein/mRNA abundances
- Identifies discordant genes
- Reports post-transcriptional regulation candidates
```

Integration Workflow [#integration-workflow]

```text
1. Load RNA-seq data (transcriptomics_expert)
2. Load proteomics data (proteomics_expert)
3. Match gene IDs between datasets
4. Calculate correlation (Pearson/Spearman)
5. Identify outliers (high mRNA, low protein)
6. Annotate with pathway enrichment
```

Supported Data Formats [#supported-data-formats]

| Software    | Format                | Support |
| ----------- | --------------------- | ------- |
| MaxQuant    | proteinGroups.txt     | Full    |
| DIA-NN      | Report.tsv            | Full    |
| Spectronaut | Report                | Full    |
| FragPipe    | combined\_protein.tsv | Full    |
| Generic     | CSV/TSV matrix        | Full    |

Services [#services]

lobster-proteomics includes domain-specific services bundled with the package:

| Service                            | Purpose                                |
| ---------------------------------- | -------------------------------------- |
| **ProteomicsAnalysisService**      | Core proteomics analysis pipeline      |
| **ProteomicsDifferentialService**  | Differential abundance analysis        |
| **ProteomicsPreprocessingService** | Data normalization and imputation      |
| **ProteomicsQualityService**       | Quality control and filtering          |
| **ProteomicsVisualizationService** | Proteomics-specific visualizations     |
| **ProteomicsSurvivalService**      | Survival analysis with protein markers |
| **WGCNALiteService**               | Protein co-expression network analysis |

Data Parsers [#data-parsers]

| Parser                | Format                     |
| --------------------- | -------------------------- |
| **MaxQuantParser**    | MaxQuant proteinGroups.txt |
| **DIANNParser**       | DIA-NN Report.tsv          |
| **SpectronautParser** | Spectronaut output         |
| **OlinkParser**       | Olink NPX data             |

Services and parsers are installed automatically with the agent package.

Dependencies [#dependencies]

lobster-proteomics requires proteomics analysis libraries:

| Library          | Purpose                            |
| ---------------- | ---------------------------------- |
| **pandas**       | Data manipulation                  |
| **numpy**        | Numerical computations             |
| **scipy**        | Statistical analysis               |
| **scikit-learn** | Machine learning and normalization |
| **statsmodels**  | Statistical modeling               |

These are installed automatically with the package.

Sub-Agent Architecture [#sub-agent-architecture]

```text
proteomics_expert (supervisor-accessible)
├── proteomics_de_analysis_expert (sub-agent, not directly accessible)
└── biomarker_discovery_expert (sub-agent, not directly accessible)
```

The proteomics\_expert handles QC, preprocessing, normalization, and pattern analysis directly. When the user requests differential expression, time course, or correlation analysis, it delegates to proteomics\_de\_analysis\_expert. For network analysis (WGCNA) and survival analysis, it delegates to biomarker\_discovery\_expert.

Configuration [#configuration]

```toml
# .lobster_workspace/config.toml
enabled = ["proteomics_expert"]
```

Access [#access]

lobster-proteomics is free and open source. Install and use without any license or activation.

Quantification Methods [#quantification-methods]

| Method    | Description               | Use Case                 |
| --------- | ------------------------- | ------------------------ |
| **LFQ**   | Label-free quantification | Most common, no labeling |
| **TMT**   | Tandem mass tags          | Multiplexed samples      |
| **iTRAQ** | Isobaric tags             | Multiplexed samples      |
| **SILAC** | Stable isotope labeling   | Metabolic labeling       |

<NextSteps
  items={[
{
  href: "/docs/agents/transcriptomics",
  title: "Transcriptomics Agent",
  description: "Multi-omics integration with RNA-seq data",
  icon: <Rocket />
},
{
  href: "/docs/agents/ml",
  title: "Machine Learning",
  description: "Feature selection and survival analysis with protein markers",
  icon: <GraduationCap />
},
{
  href: "/docs/guides/data-loading",
  title: "Data Loading Guide",
  description: "Loading MS data from various platforms",
  icon: <Database />
}
]}
/>