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Proteomics

Proteomics analysis toolkit for label-free quantitative proteomics. Invokes R scripts for normalization, visualization (volcano, heatmap, PCA, LOPIT), pathway analysis (KEGG, ConsensusPathDB), and protein list cross-referencing (MISEV2018, SASP, Matrisome). USE WHEN user says 'analyze proteomics', 'volcano plot', 'normalize protein data', 'pathway enrichment', 'check EV markers', 'SASP analysis', 'matrisome', OR mentions q-value, fold-change, or protein quantification.

personAuthor: jakexiaohubgithub
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Proteomics

Quantitative proteomics analysis toolkit combining R script invocation with embedded methodology knowledge. Fully portable - all scripts and reference data included.

Skill Directory: ~/.claude/Skills/Proteomics/

Workflow Routing

When executing a workflow, output this notification:

Running the **WorkflowName** workflow from the **Proteomics** skill...

| Workflow | Trigger | File | |----------|---------|------| | Normalize | "normalize data", "apply normalization", "median/quantile/loess normalize" | workflows/Normalize.md | | VolcanoPlot | "volcano plot", "create volcano", "visualize fold change" | workflows/VolcanoPlot.md | | Heatmap | "heatmap", "PCA", "correlation plot", "sample clustering" | workflows/Heatmap.md | | PathwayAnalysis | "pathway analysis", "KEGG enrichment", "ConsensusPathDB", "GO enrichment" | workflows/PathwayAnalysis.md | | ProteinListQuery | "check EV markers", "MISEV proteins", "exosome markers", "blood contaminants" | workflows/ProteinListQuery.md | | ExcelWorkup | "create Excel report", "filter by q-value", "generate data tables" | workflows/ExcelWorkup.md | | Matrisome | "matrisome analysis", "ECM proteins", "extracellular matrix" | workflows/Matrisome.md | | SaspAnalysis | "SASP analysis", "senescence factors", "core SASP" | workflows/SaspAnalysis.md |

Examples

Example 1: Generate Volcano Plot

User: "Create a volcano plot for my proteomics comparison data"
-> Invokes VolcanoPlot workflow
-> Asks for data file location and parameters (q-value, fold-change threshold)
-> Either invokes Plot_Workup_V10.R or generates custom ggplot2 code
-> Outputs TIFF files to output/ directory

Example 2: Check for EV Markers

User: "Which MISEV2018 EV markers are in my dataset?"
-> Invokes ProteinListQuery workflow
-> Reads user's protein list
-> Cross-references against data/MISEV2018_EV_Markers.txt
-> Returns categorized matches (Category 1-5, tetraspanins, annexins, etc.)

Example 3: Full Analysis Pipeline

User: "Run a complete proteomics analysis on my kidney data"
-> Sequences multiple workflows:
  1. Normalize (median normalization)
  2. Heatmap (PCA, sample correlation)
  3. VolcanoPlot (for each comparison)
  4. Matrisome (ECM protein analysis)
  5. SaspAnalysis (if relevant)
  6. ExcelWorkup (generate report)
-> Creates organized output/ directory structure

Example 4: Pathway Enrichment

User: "Run KEGG pathway analysis on my significantly altered proteins"
-> Invokes PathwayAnalysis workflow
-> Filters to q < 0.01, |log2FC| > 0.58
-> Runs clusterProfiler or ConsensusPathDB
-> Generates dotplot visualization

R Script Quick Reference

All scripts are in the skill's rscripts/ directory.

| Script | Purpose | Key Parameters | |--------|---------|----------------| | Plot_Workup_V10.R | Full visualization pipeline | organism, batch, myFC, myQval, mypattern | | Excel_Workup_v05.R | Excel report generation | myoutput, batch, myFC, q-value flags | | normalization/Step_1_Normalization.R | Data normalization | Input matrix (iMat) | | ConsensusPathDB_23_0411_v03.R | Pathway dotplots | input_dir, output_dir, q.val, t.level | | toolkit.R | Library loading | Called at start of analysis | | barplots.R | Bar plot utility | Various |

Standard Parameters

| Parameter | Typical Values | Description | |-----------|---------------|-------------| | q-value | 0.05, 0.01, 0.001 | Statistical significance threshold | | Fold Change | 0.58 (1.5x), 1.0 (2x) | Log2 fold change cutoff | | Organism | "human", "mouse" | Species for reference lists | | Pattern | "JB\\d_\\d+" | Regex for sample ID extraction |

Reference Data Available

All protein lists are in the skill's data/ directory.

| List | File | Contents | |------|------|----------| | MISEV2018 EV Markers | MISEV2018_EV_Markers.txt | 500+ proteins, Category 1-5 | | EV Categories | MISEV2018_EV_Categories.txt | Category definitions | | Exosome Markers | Exosome_Protein_Markers.txt | CD63, CD81, CD9, TSG101, etc. | | Blood Contaminants | Top_10_Blood_Proteins.txt | Albumin, IgG, fibrinogen, etc. | | Apolipoproteins | Apolipoproteins.txt | APOA1, APOB, etc. | | Human Core SASP | Human_Core_SASP.csv | 175 SASP factors with IR/RAS/ATV scores | | Mouse Core SASP | Mouse_Core_SASP.csv | Mouse SASP orthologs | | Human Matrisome | matrisome_hs_masterlist.csv | ECM proteins by category | | Mouse Matrisome | matrisome_mm_masterlist.csv | Mouse ECM proteins |

Required Data Structure

For running the full analysis scripts, data should be organized as:

[PROJECT_DIR]/
├── data/
│   ├── [batch]_Protein_Report_2pep.csv    # Protein intensities
│   ├── [batch]_candidates_2pep.csv         # Comparison results
│   └── [batch]_ConditionSetup.csv          # Sample metadata
└── output/
    ├── Data_Tables/                        # Excel reports
    └── [plots will be saved here]

Invocation Pattern

To run R scripts from this skill:

cd [PROJECT_WORKING_DIR]
Rscript ~/.claude/Skills/Proteomics/rscripts/[SCRIPT_NAME].R

Important: Scripts expect:

  1. Working directory set to project folder
  2. data/ subdirectory with input files
  3. output/ subdirectory for results
  4. Reference data paths point to skill's data/ directory (may need adjustment)

When NOT to Use This Skill

  • General R coding questions -> Use standard Claude
  • Non-proteomics data analysis -> Use appropriate tools
  • Genomics/transcriptomics -> Different methodology
  • Statistical consulting without data -> Explain methodology, don't run