BI
Data & Repro
bio-metabolomics-lipidomics
Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSear…
FreedomIntelligence/OpenClaw-Medical-SkillsView
Data & Repro
BI
bio-proteomics-quantification
Maintainer FreedomIntelligence · Last updated April 1, 2026
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-proteomics-quantification
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- R: MSstats::dataProcess() for feature-to-protein summarization.
- Python: pandas for MaxLFQ-style normalization and ratio calculation.
- R: MSnbase for isobaric tag reporter ion extraction.
- Quantify proteins from my mass spec data" → Extract protein abundances from MS data using label-free (LFQ, spectral counting), isobaric labeling (TMT, iTRAQ), or metabolic labeling (SILAC) approaches. R: MSstats::dataProcess() for feature-to-protein summarization Python: pandas for MaxLFQ-style normalization and ratio calculation R: MSnbase for isobaric tag reporter ion extraction.
- tmt_data <- readMSnSet('tmt_data.txt').
Source Doc
Excerpt From SKILL.md
Normalize with reference channel
tmt_normalized <- normalize(tmt_data, method = 'center.median')
Summarize to protein level
protein_data <- combineFeatures(tmt_normalized, groupBy = fData(tmt_data)$protein, fun = 'median')
## SILAC Quantification
```python
def calculate_silac_ratio(heavy_intensity, light_intensity):
'''Calculate SILAC H/L ratio'''
if light_intensity > 0 and heavy_intensity > 0:
return np.log2(heavy_intensity / light_intensity)
return np.nan
Use cases
- Use when extracting protein abundances from MS data for differential analysis.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- data-import - Load MS data before quantification
- differential-abundance - Statistical testing after quantification
- expression-matrix/counts-ingest - Similar matrix handling
Related skills
Related skills
BI
Data & Repro
bio-metabolomics-metabolite-annotation
Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment. U…
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-metabolomics-msdial-preprocessing
MS-DIAL-based metabolomics preprocessing as alternative to XCMS. Covers peak detection, alignment, annotation, and export for downstream ana…
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & Repro
bio-metabolomics-pathway-mapping
Map metabolites to biological pathways using KEGG, Reactome, and MetaboAnalyst. Perform pathway enrichment and topology analysis. Use when i…
FreedomIntelligence/OpenClaw-Medical-SkillsView