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:

code

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

code

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

code

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

code

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

code

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:

code

[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:

bash

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 files
•output/ 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