# Msstatslip

> MSstatsLiP

- Skill: `biomate-ai/msstatslip` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/msstatslip`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/msstatslip/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/msstatslip

---


# MSstatsLiP

## Workflows

### Proteolytic Resistance Analysis

```r
library(MSstatsLiP)

# Calculate proteolytic resistance ratios and perform differential analysis
Accessibility <- calculateProteolyticResistance(
  MSstatsLiP_Summarized, 
  fasta_file, 
  differential_analysis = TRUE
)

# Plot proteolytic resistance results as a barcode along the protein sequence
ResistanceBarcodePlotLiP(
  Accessibility, 
  fasta_file, 
  which.prot = "P16622", 
  which.condition = "F1", 
  differential_analysis = TRUE, 
  which.comp = "F1 vs F2", 
  address = FALSE
)
```
Input: Summarized LiP-MS and TrP-MS datasets (`MSstatsLiP_Summarized`) and a FASTA file (`fasta_file`).
Output: Proteolytic resistance ratios and sequence-aligned barcode plots.

### Standard Workflow

```r
library(MSstatsLiP)

# Convert raw Spectronaut data to MSstatsLiP format
msstats_data <- SpectronauttoMSstatsLiPFormat(
  raw_lip, 
  fasta_file, 
  raw_prot
)

# Summarize the preprocessed data
MSstatsLiP_Summarized <- dataSummarizationLiP(
  msstats_data, 
  normalization.LiP = "equalizeMedians"
)

# Perform group comparison modeling
MSstatsLiP_model <- groupComparisonLiP(MSstatsLiP_Summarized)
```
Input: Raw Spectronaut LiP and TrP datasets, and a FASTA file.
Output: Preprocessed, summarized, and modeled LiP-MS and TrP-MS datasets.

## When to Use
- Analyzing Limited Proteolysis-coupled Mass Spectrometry (LiP-MS) datasets alongside Trypsin-only control (TrP-MS) datasets.
- Preprocessing Spectronaut exports using `SpectronauttoMSstatsLiPFormat`.
- Performing differential analysis of proteolytic resistance patterns across conditions using `calculateProteolyticResistance` and `groupComparisonLiP`.
- Visualizing peptide-level proteolytic resistance changes along a protein sequence using `ResistanceBarcodePlotLiP`.

## When NOT to Use
- For standard bottom-up shotgun proteomics data without limited proteolysis, use `MSstats` directly.
- For differential expression analysis of RNA-seq data, use packages like `limma`, `edgeR`, or `DESeq2`.

## Data Requirements
- Raw LiP-MS and TrP-MS datasets (e.g., Spectronaut exports) containing columns like `PG.ProteinAccessions`, `PEP.GroupingKey`, `EG.Qvalue`, and `F.PeakArea`.
- A FASTA file containing the protein sequences of interest.

## Key Parameters
- **normalization.LiP** ("equalizeMedians"): Normalization method for LiP data in `dataSummarizationLiP`.
- **differential_analysis** (TRUE): Whether to perform differential analysis in `calculateProteolyticResistance`.
- **which.prot** (NULL): Protein accession to plot in `ResistanceBarcodePlotLiP`.
- **which.condition** (NULL): Condition to plot in `ResistanceBarcodePlotLiP`.
- **address** (FALSE): Whether to save the plot to a file or display it in `ResistanceBarcodePlotLiP`.

## Best Practices
- Filter out half-tryptic (HT) peptides and retain only fully tryptic (FT) peptides for proteolytic resistance analysis using `calculateTrypticity`.
- Ensure that the Condition nomenclature is identical in both LiP and TrP datasets before summarization.
- Ensure unique BioReplicate nomenclature for case-control experiments.

## Common Pitfalls
- Mismatched condition names between LiP and TrP datasets, causing errors in summarization or modeling; verify using `unique(msstats_data[["LiP"]]$Condition) %in% unique(msstats_data[["TrP"]]$Condition)`.
- High memory usage during summarization; clear memory cache using `rm()` and `gc()` if needed.

## Alternatives
- `MSstats` for standard proteomics without limited proteolysis.
- `limma` for general linear modeling of expression data.

## Citations
- Cappelletti et al., 2021 (referenced in vignette text)

## References
- Homepage: bioconductor.org/packages/MSstatsLiP
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html