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:
- 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)
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
1---2name: proteomics3description: 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.4---5
6# Proteomics
7
8Quantitative proteomics analysis toolkit combining R script invocation with embedded methodology knowledge. Fully portable - all scripts and reference data included.
9
10**Skill Directory:** `~/.claude/Skills/Proteomics/`
11
12---
13
14## Workflow Routing
15
16**When executing a workflow, output this notification:**
17
18```
19Running the **WorkflowName** workflow from the **Proteomics** skill...
20```
21
22| Workflow | Trigger | File |
23|----------|---------|------|
24| **Normalize** | "normalize data", "apply normalization", "median/quantile/loess normalize" | `workflows/Normalize.md` |
25| **VolcanoPlot** | "volcano plot", "create volcano", "visualize fold change" | `workflows/VolcanoPlot.md` |
26| **Heatmap** | "heatmap", "PCA", "correlation plot", "sample clustering" | `workflows/Heatmap.md` |
27| **PathwayAnalysis** | "pathway analysis", "KEGG enrichment", "ConsensusPathDB", "GO enrichment" | `workflows/PathwayAnalysis.md` |
28| **ProteinListQuery** | "check EV markers", "MISEV proteins", "exosome markers", "blood contaminants" | `workflows/ProteinListQuery.md` |
29| **ExcelWorkup** | "create Excel report", "filter by q-value", "generate data tables" | `workflows/ExcelWorkup.md` |
30| **Matrisome** | "matrisome analysis", "ECM proteins", "extracellular matrix" | `workflows/Matrisome.md` |
31| **SaspAnalysis** | "SASP analysis", "senescence factors", "core SASP" | `workflows/SaspAnalysis.md` |
32
33---
34
35## Examples
36
37**Example 1: Generate Volcano Plot**
38```
39User: "Create a volcano plot for my proteomics comparison data"
40-> Invokes VolcanoPlot workflow
41-> Asks for data file location and parameters (q-value, fold-change threshold)
42-> Either invokes Plot_Workup_V10.R or generates custom ggplot2 code
43-> Outputs TIFF files to output/ directory
44```
45
46**Example 2: Check for EV Markers**
47```
48User: "Which MISEV2018 EV markers are in my dataset?"
49-> Invokes ProteinListQuery workflow
50-> Reads user's protein list
51-> Cross-references against data/MISEV2018_EV_Markers.txt
52-> Returns categorized matches (Category 1-5, tetraspanins, annexins, etc.)
53```
54
55**Example 3: Full Analysis Pipeline**
56```
57User: "Run a complete proteomics analysis on my kidney data"
58-> Sequences multiple workflows:
59 1. Normalize (median normalization)
60 2. Heatmap (PCA, sample correlation)
61 3. VolcanoPlot (for each comparison)
62 4. Matrisome (ECM protein analysis)
63 5. SaspAnalysis (if relevant)
64 6. ExcelWorkup (generate report)
65-> Creates organized output/ directory structure
66```
67
68**Example 4: Pathway Enrichment**
69```
70User: "Run KEGG pathway analysis on my significantly altered proteins"
71-> Invokes PathwayAnalysis workflow
72-> Filters to q < 0.01, |log2FC| > 0.58
73-> Runs clusterProfiler or ConsensusPathDB
74-> Generates dotplot visualization
75```
76
77---
78
79## R Script Quick Reference
80
81All scripts are in the skill's `rscripts/` directory.
82
83| Script | Purpose | Key Parameters |
84|--------|---------|----------------|
85| `Plot_Workup_V10.R` | Full visualization pipeline | `organism`, `batch`, `myFC`, `myQval`, `mypattern` |
86| `Excel_Workup_v05.R` | Excel report generation | `myoutput`, `batch`, `myFC`, q-value flags |
87| `normalization/Step_1_Normalization.R` | Data normalization | Input matrix (iMat) |
88| `ConsensusPathDB_23_0411_v03.R` | Pathway dotplots | `input_dir`, `output_dir`, `q.val`, `t.level` |
89| `toolkit.R` | Library loading | Called at start of analysis |
90| `barplots.R` | Bar plot utility | Various |
91
92---
93
94## Standard Parameters
95
96| Parameter | Typical Values | Description |
97|-----------|---------------|-------------|
98| q-value | 0.05, 0.01, 0.001 | Statistical significance threshold |
99| Fold Change | 0.58 (1.5x), 1.0 (2x) | Log2 fold change cutoff |
100| Organism | "human", "mouse" | Species for reference lists |
101| Pattern | `"JB\\d_\\d+"` | Regex for sample ID extraction |
102
103---
104
105## Reference Data Available
106
107All protein lists are in the skill's `data/` directory.
108
109| List | File | Contents |
110|------|------|----------|
111| MISEV2018 EV Markers | `MISEV2018_EV_Markers.txt` | 500+ proteins, Category 1-5 |
112| EV Categories | `MISEV2018_EV_Categories.txt` | Category definitions |
113| Exosome Markers | `Exosome_Protein_Markers.txt` | CD63, CD81, CD9, TSG101, etc. |
114| Blood Contaminants | `Top_10_Blood_Proteins.txt` | Albumin, IgG, fibrinogen, etc. |
115| Apolipoproteins | `Apolipoproteins.txt` | APOA1, APOB, etc. |
116| Human Core SASP | `Human_Core_SASP.csv` | 175 SASP factors with IR/RAS/ATV scores |
117| Mouse Core SASP | `Mouse_Core_SASP.csv` | Mouse SASP orthologs |
118| Human Matrisome | `matrisome_hs_masterlist.csv` | ECM proteins by category |
119| Mouse Matrisome | `matrisome_mm_masterlist.csv` | Mouse ECM proteins |
120
121---
122
123## Required Data Structure
124
125For running the full analysis scripts, data should be organized as:
126
127```
128[PROJECT_DIR]/
129├── data/
130│ ├── [batch]_Protein_Report_2pep.csv # Protein intensities
131│ ├── [batch]_candidates_2pep.csv # Comparison results
132│ └── [batch]_ConditionSetup.csv # Sample metadata
133└── output/
134 ├── Data_Tables/ # Excel reports
135 └── [plots will be saved here]
136```
137
138---
139
140## Invocation Pattern
141
142**To run R scripts from this skill:**
143```bash
144cd [PROJECT_WORKING_DIR]
145Rscript ~/.claude/Skills/Proteomics/rscripts/[SCRIPT_NAME].R
146```
147
148**Important:** Scripts expect:
1491. Working directory set to project folder
1502. `data/` subdirectory with input files
1513. `output/` subdirectory for results
1524. Reference data paths point to skill's `data/` directory (may need adjustment)
153
154---
155
156## When NOT to Use This Skill
157
158- General R coding questions -> Use standard Claude
159- Non-proteomics data analysis -> Use appropriate tools
160- Genomics/transcriptomics -> Different methodology
161- Statistical consulting without data -> Explain methodology, don't run
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