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/
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
|
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
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 |
| 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 |
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 |
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]
To run R scripts from this skill:
cd [PROJECT_WORKING_DIR]
Rscript ~/.claude/Skills/Proteomics/rscripts/[SCRIPT_NAME].R
Important: Scripts expect:
- Working directory set to project folder
data/
subdirectory with input filesoutput/
subdirectory for results- Reference data paths point to skill's
data/
directory (may need adjustment)
- 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